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| # Research model card · Observatory of baseline classifiers | |
| ## Models | |
| - Multinomial Naive Bayes (token-count-based deterministic baseline) | |
| - Word TF-IDF centroid | |
| - Character TF-IDF centroid | |
| - Hybrid Word/Character TF-IDF centroid | |
| These four research baselines are trained by `source_modelops_engine.js` on 842 synthetic-curated sentences. The current dashboard reads their **precomputed predictions** and does not train models when the page loads. The supplied regeneration script performs the same deterministic training in Node.js. | |
| ## Intended use | |
| Educational demonstrations of model evaluation, performance shifts, error analysis, reproducible benchmarking and model-governance documentation. Suitable as a portfolio research tool. | |
| ## Not intended for | |
| Real-world document assessment, personnel or education decisions, deployment certification, bias/fairness conclusions, real-time monitoring, or unverified inferential claims about unseen scientific corpora. | |
| ## Test results | |
| See `README.md` and `evaluation_snapshots.json`. Report results by split and sample size. Controlled IID metrics should not be substituted for challenge performance. | |
| ## Model limitations | |
| Synthetic-curated data, template structure, possible annotation shortcuts, 54-case challenge size, narrow task design, and no systematic external domain transfer validation within this project. Probability calibration is **not** established. The dashboard shows accuracy and macro-F1, not calibrated confidence. | |