PHYSICAL ACTIVITY: PREVENTION AND SUPPORT IN THE FIGHT AGAINST DEPRESSION
Bibliographic record
Abstract
AMA Ochotnicka A, Marcinkiewicz A. PHYSICAL ACTIVITY: PREVENTION AND SUPPORT IN THE FIGHT AGAINST DEPRESSION. Health Problems of Civilization. 2024;18(1):3-4. doi:10.5114/hpc.2024.134586. APA Ochotnicka, A., & Marcinkiewicz, A. (2024). PHYSICAL ACTIVITY: PREVENTION AND SUPPORT IN THE FIGHT AGAINST DEPRESSION. Health Problems of Civilization, 18(1), 3-4. https://doi.org/10.5114/hpc.2024.134586 Chicago Ochotnicka, Aleksandra, and Anita Marcinkiewicz. 2024. "PHYSICAL ACTIVITY: PREVENTION AND SUPPORT IN THE FIGHT AGAINST DEPRESSION". Health Problems of Civilization 18 (1): 3-4. doi:10.5114/hpc.2024.134586. Harvard Ochotnicka, A., and Marcinkiewicz, A. (2024). PHYSICAL ACTIVITY: PREVENTION AND SUPPORT IN THE FIGHT AGAINST DEPRESSION. Health Problems of Civilization, 18(1), pp.3-4. https://doi.org/10.5114/hpc.2024.134586 MLA Ochotnicka, Aleksandra et al. "PHYSICAL ACTIVITY: PREVENTION AND SUPPORT IN THE FIGHT AGAINST DEPRESSION." Health Problems of Civilization, vol. 18, no. 1, 2024, pp. 3-4. doi:10.5114/hpc.2024.134586. Vancouver Ochotnicka A, Marcinkiewicz A. PHYSICAL ACTIVITY: PREVENTION AND SUPPORT IN THE FIGHT AGAINST DEPRESSION. Health Problems of Civilization. 2024;18(1):3-4. doi:10.5114/hpc.2024.134586.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".