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# import re
# import json
# import pickle
# import warnings
# import numpy as np
# from pathlib import Path
# from dataclasses import dataclass, field
# from typing import List
# warnings.filterwarnings("ignore")
# MODEL_DIR = Path(__file__).resolve().parent.parent.parent.parent / "models" / "v2"
# CONFIG_PATH = MODEL_DIR / "ensemble_config_v2.json"
# _config = None
# _models = None
# def _load_assets():
# global _config, _models
# if _models is not None:
# return
# with open(CONFIG_PATH) as fh:
# _config = json.load(fh)
# names = ["lgbm_v2", "xgb_v2", "rf_v2", "lr_v2"]
# _models = {}
# for name in names:
# path = MODEL_DIR / f"{name}.pkl"
# if not path.exists():
# raise FileNotFoundError(f"Model file missing: {path}")
# with open(path, "rb") as fh:
# _models[name] = pickle.load(fh)
# def _extract_features(text: str, title: str = "", journal: str = "", subject: str = "") -> np.ndarray:
# t = text.lower()
# tt = title.lower()
# features = {}
# # basic text stats
# words = t.split()
# features["word_count"] = len(words)
# features["text_length"] = len(text)
# features["title_length"] = len(title.split()) if title else 0
# features["vocab_diversity"] = len(set(words)) / max(len(words), 1)
# # p-values
# pvals = re.findall(r'p\s*[<=]\s*0\.0\d+', t)
# features["n_pvalues"] = len(pvals)
# features["has_p05"] = 1.0 if re.search(r'p\s*[<=]\s*0\.05', t) else 0.0
# features["pval_cluster"] = 1.0 if len(pvals) > 4 else 0.0
# features["sig_word_count"] = t.count("significant")
# # sample sizes
# samples = re.findall(r'\bn\s*=\s*(\d+)', t)
# features["n_samples_found"] = len(samples)
# if samples:
# sizes = [int(x) for x in samples]
# features["min_sample"] = min(sizes)
# features["max_sample"] = max(sizes)
# features["tiny_sample"] = 1.0 if min(sizes) < 20 else 0.0
# else:
# features["min_sample"] = 0
# features["max_sample"] = 0
# features["tiny_sample"] = 0.0
# # numbers
# all_nums = re.findall(r'\b\d+\.?\d*\b', t)
# features["number_density"] = len(all_nums) / max(len(words), 1)
# features["n_numbers"] = len(all_nums)
# if all_nums:
# nums = [float(x) for x in all_nums[:300]]
# rounds = sum(1 for n in nums if n > 0 and n == int(n) and int(n) % 5 == 0)
# features["round_number_ratio"] = rounds / max(len(nums), 1)
# terms = [int(str(int(n))[-1]) for n in nums if n > 9]
# features["terminal_zero_bias"] = terms.count(0) / max(len(terms), 1) if terms else 0.0
# else:
# features["round_number_ratio"] = 0.0
# features["terminal_zero_bias"] = 0.0
# # ai phrase signals
# ai_phrases = [
# "it is worth noting", "importantly", "furthermore", "in conclusion",
# "it should be noted", "notably", "these findings suggest",
# "our results demonstrate", "taken together", "in summary",
# "delve", "comprehensive", "robust", "novel approach",
# "state-of-the-art", "leverage", "utilize", "facilitate",
# "shedding light", "in this context", "it is noteworthy"
# ]
# features["ai_phrase_count"] = sum(1 for p in ai_phrases if p in t)
# features["ai_phrase_density"] = features["ai_phrase_count"] / max(len(words), 1)
# # sentence structure
# sents = [s.strip() for s in re.split(r'[.!?]+', text) if len(s.strip()) > 20]
# if len(sents) > 2:
# lens = [len(s.split()) for s in sents]
# features["sentence_uniformity"] = 1.0 - (np.std(lens) / max(np.mean(lens), 1))
# features["avg_sentence_len"] = float(np.mean(lens))
# features["burstiness"] = float(np.std(lens) / max(np.mean(lens), 1))
# features["n_sentences"] = len(sents)
# else:
# features["sentence_uniformity"] = 0.5
# features["avg_sentence_len"] = 15.0
# features["burstiness"] = 0.5
# features["n_sentences"] = len(sents)
# # methodology
# method_good = ["randomized", "control group", "placebo", "double-blind",
# "pre-registered", "blinded", "confound", "limitation"]
# method_bad = ["we prove", "our study proves", "conclusively shows",
# "undeniably", "without doubt", "clearly demonstrates"]
# features["method_good_count"] = sum(1 for w in method_good if w in t)
# features["method_bad_count"] = sum(1 for w in method_bad if w in t)
# features["methodology_score"] = features["method_good_count"] / len(method_good)
# # citations
# cites = re.findall(r'\[\d+\]|\(\w+\s*,\s*\d{4}\)', text)
# features["n_citations"] = len(cites)
# features["citation_density"] = len(cites) / max(len(words), 1)
# self_cite = ["our previous", "our earlier", "we previously", "we reported"]
# features["self_cite_signals"] = sum(1 for w in self_cite if w in t)
# # reproducibility
# repro_pos = ["github", "zenodo", "figshare", "data available",
# "code available", "open source", "supplementary"]
# repro_neg = ["upon request", "data not available", "available on request"]
# features["repro_positive"] = sum(1 for w in repro_pos if w in t)
# features["repro_negative"] = sum(1 for w in repro_neg if w in t)
# # title signals
# title_hype = ["novel", "innovative", "significant", "first", "groundbreaking",
# "unprecedented", "revolutionary", "remarkable"]
# features["title_hype_count"] = sum(1 for w in title_hype if w in tt)
# features["title_has_numbers"] = 1.0 if re.search(r'\d', title) else 0.0
# # metadata signals (0 if not provided)
# predatory = ["frontiers", "mdpi", "hindawi", "sciencepg", "omics",
# "scirp", "waset", "ijser", "iiste"]
# features["predatory_journal"] = 1.0 if any(p in journal.lower() for p in predatory) else 0.0
# high_risk_subj = ["biology - cellular", "genetics", "biochemistry",
# "cancer", "computer science", "data science"]
# features["high_risk_subject"] = 1.0 if any(s in subject.lower() for s in high_risk_subj) else 0.0
# # benford deviation
# dec_nums = re.findall(r'\b\d+\.\d+\b', t)
# if len(dec_nums) >= 5:
# first_d = [str(float(n))[0] for n in dec_nums if float(n) > 0]
# first_d = [d for d in first_d if d.isdigit() and d != '0']
# if first_d:
# counts = [first_d.count(str(d)) for d in range(1, 10)]
# total = sum(counts)
# obs = [c / total for c in counts]
# exp = [np.log10(1 + 1 / d) for d in range(1, 10)]
# features["benford_deviation"] = float(sum(abs(o - e) for o, e in zip(obs, exp)))
# else:
# features["benford_deviation"] = 0.0
# else:
# features["benford_deviation"] = 0.0
# # composite
# features["high_risk_combo"] = (
# (1 if features["pval_cluster"] else 0) +
# (1 if features["ai_phrase_count"] > 4 else 0) +
# (1 if features["round_number_ratio"] > 0.5 else 0) +
# (1 if features["predatory_journal"] else 0)
# ) / 4.0
# features["integrity_index"] = (
# features["methodology_score"] * 0.20 +
# features["repro_positive"] * 0.02 +
# features["vocab_diversity"] * 0.15 +
# features["burstiness"] * 0.15 +
# (1 - features["round_number_ratio"]) * 0.15 +
# max(0, 1 - features["ai_phrase_density"] * 8) * 0.10 +
# (1 - features["predatory_journal"]) * 0.10 +
# features["method_good_count"] * 0.02
# )
# feat_order = _config["features"]
# return np.array([features.get(k, 0.0) for k in feat_order], dtype=np.float64)
# def _interpret(replication_prob: float) -> tuple:
# if replication_prob >= 0.75:
# level = "HIGH"
# verdict = "Strong indicators of replicability detected across linguistic signals."
# elif replication_prob >= 0.50:
# level = "MODERATE"
# verdict = "Replication plausible but some integrity concerns present."
# elif replication_prob >= 0.30:
# level = "LOW"
# verdict = "Multiple integrity signals suggest replication difficulty."
# else:
# level = "VERY LOW"
# verdict = "Serious integrity concerns — independent replication unlikely without raw data."
# return level, verdict
# @dataclass
# class ReplicationResult:
# module: str = "Replication Probability Score"
# replication_probability: float = 0.0
# fraud_probability: float = 0.0
# replication_level: str = "UNKNOWN"
# verdict: str = ""
# risk_score: float = 0.0
# risk_level: str = "UNKNOWN"
# summary: str = ""
# flags: List[dict] = field(default_factory=list)
# flags_count: int = 0
# model_version: str = "2.0.0"
# ensemble_auc: float = 0.895
# error: str = ""
# def analyze(scores: dict, text: str = "", title: str = "",
# journal: str = "", subject: str = "") -> ReplicationResult:
# result = ReplicationResult()
# try:
# _load_assets()
# feat_vec = _extract_features(text, title, journal, subject)
# X = feat_vec.reshape(1, -1)
# w = _config["weights"]
# thresh = _config["threshold"]
# p_lgbm = float(_models["lgbm_v2"].predict_proba(X)[0][1])
# p_xgb = float(_models["xgb_v2"].predict_proba(X)[0][1])
# p_rf = float(_models["rf_v2"].predict_proba(X)[0][1])
# p_lr = float(_models["lr_v2"].predict_proba(X)[0][1])
# fraud_prob = (p_lgbm * w["lgbm"] + p_xgb * w["xgb"] +
# p_rf * w["rf"] + p_lr * w["lr"])
# replic_prob = round(1.0 - fraud_prob, 4)
# fraud_prob = round(fraud_prob, 4)
# level, verdict = _interpret(replic_prob)
# result.replication_probability = replic_prob
# result.fraud_probability = fraud_prob
# result.replication_level = level
# result.verdict = verdict
# result.risk_score = fraud_prob
# result.risk_level = (
# "LOW" if fraud_prob < 0.35 else
# "MEDIUM" if fraud_prob < 0.60 else
# "HIGH"
# )
# result.summary = (
# f"Ensemble ML replication probability: {replic_prob:.1%}. "
# f"Fraud likelihood: {fraud_prob:.1%}. "
# f"Confidence level: {level}. "
# f"Model AUC: 0.895 on 91,779 papers."
# )
# flags = []
# feat_vec_named = dict(zip(_config["features"], feat_vec.tolist()))
# if feat_vec_named.get("predatory_journal", 0) > 0:
# flags.append({
# "flag_type": "Predatory Journal Signal",
# "severity": "HIGH",
# "description": "Journal name matches known predatory publisher patterns.",
# "evidence": f"journal={journal}",
# "suggestion": "Verify journal indexing in DOAJ or Scopus before citing.",
# })
# if feat_vec_named.get("ai_phrase_count", 0) > 4:
# flags.append({
# "flag_type": "AI-Generated Content Pattern",
# "severity": "MEDIUM",
# "description": "High density of AI-typical phrases detected in abstract.",
# "evidence": f"ai_phrase_count={feat_vec_named['ai_phrase_count']:.0f}",
# "suggestion": "Cross-check methodology section for AI-generated boilerplate.",
# })
# if feat_vec_named.get("methodology_score", 0) < 0.05:
# flags.append({
# "flag_type": "Weak Methodology Reporting",
# "severity": "MEDIUM",
# "description": "Abstract contains few standard methodology terms.",
# "evidence": f"methodology_score={feat_vec_named['methodology_score']:.3f}",
# "suggestion": "Request full methods section before citation.",
# })
# if feat_vec_named.get("repro_positive", 0) == 0:
# flags.append({
# "flag_type": "No Reproducibility Statement",
# "severity": "LOW",
# "description": "No data availability or code sharing mentioned.",
# "evidence": "repro_positive=0",
# "suggestion": "Contact authors for data and code availability.",
# })
# if replic_prob < 0.35:
# flags.append({
# "flag_type": "Low Replication Probability",
# "severity": "HIGH",
# "description": "Ensemble model signals high fraud likelihood from text patterns.",
# "evidence": f"fraud_prob={fraud_prob:.3f}, threshold={thresh:.2f}",
# "suggestion": "Do not replicate without obtaining raw data from authors.",
# })
# result.flags = flags
# result.flags_count = len(flags)
# except Exception as exc:
# result.error = str(exc)
# result.summary = f"Replication analysis failed: {exc}"
# return result
import re
import json
import pickle
import warnings
import numpy as np
from pathlib import Path
from dataclasses import dataclass, field
from typing import List
warnings.filterwarnings("ignore")
MODEL_DIR = Path(__file__).resolve().parent.parent.parent.parent / "models" / "v2"
CONFIG_PATH = MODEL_DIR / "ensemble_config_v2.json"
_config = None
_models = None
def _load_assets():
global _config, _models
if _models is not None:
return
with open(CONFIG_PATH) as fh:
_config = json.load(fh)
names = ["lgbm_v2", "xgb_v2", "rf_v2", "lr_v2"]
_models = {}
for name in names:
path = MODEL_DIR / f"{name}.pkl"
if path.exists():
with open(path, "rb") as fh:
_models[name] = pickle.load(fh)
else:
_models[name] = None
def _extract_features(text: str, title: str = "", journal: str = "", subject: str = "") -> np.ndarray:
t = text.lower()
tt = title.lower()
features = {}
words = t.split()
features["word_count"] = len(words)
features["text_length"] = len(text)
features["title_length"] = len(title.split()) if title else 0
features["vocab_diversity"] = len(set(words)) / max(len(words), 1)
pvals = re.findall(r'p\s*[<=]\s*0\.0\d+', t)
features["n_pvalues"] = len(pvals)
features["has_p05"] = 1.0 if re.search(r'p\s*[<=]\s*0\.05', t) else 0.0
features["pval_cluster"] = 1.0 if len(pvals) > 4 else 0.0
features["sig_word_count"] = t.count("significant")
samples = re.findall(r'\bn\s*=\s*(\d+)', t)
features["n_samples_found"] = len(samples)
if samples:
sizes = [int(x) for x in samples]
features["min_sample"] = min(sizes)
features["max_sample"] = max(sizes)
features["tiny_sample"] = 1.0 if min(sizes) < 20 else 0.0
else:
features["min_sample"] = 0
features["max_sample"] = 0
features["tiny_sample"] = 0.0
all_nums = re.findall(r'\b\d+\.?\d*\b', t)
features["number_density"] = len(all_nums) / max(len(words), 1)
features["n_numbers"] = len(all_nums)
if all_nums:
nums = [float(x) for x in all_nums[:300]]
rounds = sum(1 for n in nums if n > 0 and n == int(n) and int(n) % 5 == 0)
features["round_number_ratio"] = rounds / max(len(nums), 1)
terms = [int(str(int(n))[-1]) for n in nums if n > 9]
features["terminal_zero_bias"] = terms.count(0) / max(len(terms), 1) if terms else 0.0
else:
features["round_number_ratio"] = 0.0
features["terminal_zero_bias"] = 0.0
ai_phrases = [
"it is worth noting", "importantly", "furthermore", "in conclusion",
"it should be noted", "notably", "these findings suggest",
"our results demonstrate", "taken together", "in summary",
"delve", "comprehensive", "robust", "novel approach",
"state-of-the-art", "leverage", "utilize", "facilitate",
"shedding light", "in this context", "it is noteworthy"
]
features["ai_phrase_count"] = sum(1 for p in ai_phrases if p in t)
features["ai_phrase_density"] = features["ai_phrase_count"] / max(len(words), 1)
sents = [s.strip() for s in re.split(r'[.!?]+', text) if len(s.strip()) > 20]
if len(sents) > 2:
lens = [len(s.split()) for s in sents]
features["sentence_uniformity"] = 1.0 - (np.std(lens) / max(np.mean(lens), 1))
features["avg_sentence_len"] = float(np.mean(lens))
features["burstiness"] = float(np.std(lens) / max(np.mean(lens), 1))
features["n_sentences"] = len(sents)
else:
features["sentence_uniformity"] = 0.5
features["avg_sentence_len"] = 15.0
features["burstiness"] = 0.5
features["n_sentences"] = len(sents)
method_good = ["randomized", "control group", "placebo", "double-blind",
"pre-registered", "blinded", "confound", "limitation"]
method_bad = ["we prove", "our study proves", "conclusively shows",
"undeniably", "without doubt", "clearly demonstrates"]
features["method_good_count"] = sum(1 for w in method_good if w in t)
features["method_bad_count"] = sum(1 for w in method_bad if w in t)
features["methodology_score"] = features["method_good_count"] / len(method_good)
cites = re.findall(r'\[\d+\]|\(\w+\s*,\s*\d{4}\)', text)
features["n_citations"] = len(cites)
features["citation_density"] = len(cites) / max(len(words), 1)
self_cite = ["our previous", "our earlier", "we previously", "we reported"]
features["self_cite_signals"] = sum(1 for w in self_cite if w in t)
repro_pos = ["github", "zenodo", "figshare", "data available",
"code available", "open source", "supplementary"]
repro_neg = ["upon request", "data not available", "available on request"]
features["repro_positive"] = sum(1 for w in repro_pos if w in t)
features["repro_negative"] = sum(1 for w in repro_neg if w in t)
title_hype = ["novel", "innovative", "significant", "first", "groundbreaking",
"unprecedented", "revolutionary", "remarkable"]
features["title_hype_count"] = sum(1 for w in title_hype if w in tt)
features["title_has_numbers"] = 1.0 if re.search(r'\d', title) else 0.0
predatory = ["frontiers", "mdpi", "hindawi", "sciencepg", "omics",
"scirp", "waset", "ijser", "iiste"]
features["predatory_journal"] = 1.0 if any(p in journal.lower() for p in predatory) else 0.0
high_risk_subj = ["biology - cellular", "genetics", "biochemistry",
"cancer", "computer science", "data science"]
features["high_risk_subject"] = 1.0 if any(s in subject.lower() for s in high_risk_subj) else 0.0
dec_nums = re.findall(r'\b\d+\.\d+\b', t)
if len(dec_nums) >= 5:
first_d = [str(float(n))[0] for n in dec_nums if float(n) > 0]
first_d = [d for d in first_d if d.isdigit() and d != '0']
if first_d:
counts = [first_d.count(str(d)) for d in range(1, 10)]
total = sum(counts)
obs = [c / total for c in counts]
exp = [np.log10(1 + 1 / d) for d in range(1, 10)]
features["benford_deviation"] = float(sum(abs(o - e) for o, e in zip(obs, exp)))
else:
features["benford_deviation"] = 0.0
else:
features["benford_deviation"] = 0.0
features["high_risk_combo"] = (
(1 if features["pval_cluster"] else 0) +
(1 if features["ai_phrase_count"] > 4 else 0) +
(1 if features["round_number_ratio"] > 0.5 else 0) +
(1 if features["predatory_journal"] else 0)
) / 4.0
features["integrity_index"] = (
features["methodology_score"] * 0.20 +
features["repro_positive"] * 0.02 +
features["vocab_diversity"] * 0.15 +
features["burstiness"] * 0.15 +
(1 - features["round_number_ratio"]) * 0.15 +
max(0, 1 - features["ai_phrase_density"] * 8) * 0.10 +
(1 - features["predatory_journal"]) * 0.10 +
features["method_good_count"] * 0.02
)
feat_order = _config["features"]
return np.array([features.get(k, 0.0) for k in feat_order], dtype=np.float64)
def _interpret(replication_prob: float) -> tuple:
if replication_prob >= 0.75:
level = "HIGH"
verdict = "Strong indicators of replicability detected across linguistic signals."
elif replication_prob >= 0.50:
level = "MODERATE"
verdict = "Replication plausible but some integrity concerns present."
elif replication_prob >= 0.30:
level = "LOW"
verdict = "Multiple integrity signals suggest replication difficulty."
else:
level = "VERY LOW"
verdict = "Serious integrity concerns — independent replication unlikely without raw data."
return level, verdict
@dataclass
class ReplicationResult:
module: str = "Replication Probability Score"
replication_probability: float = 0.0
fraud_probability: float = 0.0
replication_level: str = "UNKNOWN"
verdict: str = ""
risk_score: float = 0.0
risk_level: str = "UNKNOWN"
summary: str = ""
flags: List[dict] = field(default_factory=list)
flags_count: int = 0
model_version: str = "2.0.0"
ensemble_auc: float = 0.895
error: str = ""
def analyze(scores: dict, text: str = "", title: str = "",
journal: str = "", subject: str = "") -> ReplicationResult:
result = ReplicationResult()
try:
_load_assets()
feat_vec = _extract_features(text, title, journal, subject)
X = feat_vec.reshape(1, -1)
w = _config["weights"]
thresh = _config["threshold"]
p_lgbm = float(_models["lgbm_v2"].predict_proba(X)[0][1]) if _models.get("lgbm_v2") else None
p_xgb = float(_models["xgb_v2"].predict_proba(X)[0][1]) if _models.get("xgb_v2") else None
p_rf = float(_models["rf_v2"].predict_proba(X)[0][1]) if _models.get("rf_v2") else None
p_lr = float(_models["lr_v2"].predict_proba(X)[0][1]) if _models.get("lr_v2") else None
available = {
"lgbm": p_lgbm,
"xgb": p_xgb,
"rf": p_rf,
"lr": p_lr,
}
loaded = {k: v for k, v in available.items() if v is not None}
if not loaded:
raise RuntimeError("No ensemble models could be loaded.")
total_weight = sum(w[k] for k in loaded)
fraud_prob = sum(loaded[k] * w[k] for k in loaded) / total_weight
replic_prob = round(1.0 - fraud_prob, 4)
fraud_prob = round(fraud_prob, 4)
models_used = "+".join(k.upper() for k in loaded)
missing = [k.upper() for k in available if available[k] is None]
level, verdict = _interpret(replic_prob)
result.replication_probability = replic_prob
result.fraud_probability = fraud_prob
result.replication_level = level
result.verdict = verdict
result.risk_score = fraud_prob
result.risk_level = (
"LOW" if fraud_prob < 0.35 else
"MEDIUM" if fraud_prob < 0.60 else
"HIGH"
)
missing_note = f" (RF excluded — large file, {missing} not deployed)" if missing else ""
result.summary = (
f"Ensemble ML replication probability: {replic_prob:.1%}. "
f"Fraud likelihood: {fraud_prob:.1%}. "
f"Confidence level: {level}. "
f"Models used: {models_used}{missing_note}. "
f"AUC: 0.895 on SciPeerBench v2.0 (91,779 papers)."
)
flags = []
feat_vec_named = dict(zip(_config["features"], feat_vec.tolist()))
if feat_vec_named.get("predatory_journal", 0) > 0:
flags.append({
"flag_type": "Predatory Journal Signal",
"severity": "HIGH",
"description": "Journal name matches known predatory publisher patterns.",
"evidence": f"journal={journal}",
"suggestion": "Verify journal indexing in DOAJ or Scopus before citing.",
})
if feat_vec_named.get("ai_phrase_count", 0) > 4:
flags.append({
"flag_type": "AI-Generated Content Pattern",
"severity": "MEDIUM",
"description": "High density of AI-typical phrases detected.",
"evidence": f"ai_phrase_count={feat_vec_named['ai_phrase_count']:.0f}",
"suggestion": "Cross-check methodology section for AI-generated boilerplate.",
})
if feat_vec_named.get("methodology_score", 0) < 0.05:
flags.append({
"flag_type": "Weak Methodology Reporting",
"severity": "MEDIUM",
"description": "Abstract contains few standard methodology terms.",
"evidence": f"methodology_score={feat_vec_named['methodology_score']:.3f}",
"suggestion": "Request full methods section before citation.",
})
if feat_vec_named.get("repro_positive", 0) == 0:
flags.append({
"flag_type": "No Reproducibility Statement",
"severity": "LOW",
"description": "No data availability or code sharing mentioned.",
"evidence": "repro_positive=0",
"suggestion": "Contact authors for data and code availability.",
})
if replic_prob < 0.35:
flags.append({
"flag_type": "Low Replication Probability",
"severity": "HIGH",
"description": "Ensemble model signals high fraud likelihood from text patterns.",
"evidence": f"fraud_prob={fraud_prob:.3f}, threshold={thresh:.2f}",
"suggestion": "Do not replicate without obtaining raw data from authors.",
})
result.flags = flags
result.flags_count = len(flags)
except Exception as exc:
result.error = str(exc)
result.summary = f"Replication analysis failed: {exc}"
return result |