Dr. Christiansen, <i>et al,</i> reply
Bibliographic record
Abstract
To the Editor: We thank Dr. Sabour1 and Dr. Rothschild2 for their interest in our manuscript3. We acknowledge that κ statistics depend on the prevalence of the variable under investigation and we have made this transparent. This limitation becomes relevant when comparing results across multiple studies. However, we use κ to assess which of several variables similarly assessed on the same patients provide sufficient agreement, i.e., we primarily used κ to order lesion types. This implies that the actual value of κ is of minor importance and the above limitation does not alter the conclusion of … Address correspondence to Dr. A.A. Christiansen, Department of Research, King Christian 10th Hospital for Rheumatic Diseases, Toldbodgade 3, 6300 Gråsten, Denmark. E-mail: achristiansen{at}gigtforeningen.dk
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.023 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".