A response to letter to the editor: A genetic-based algorithm for personalized resistance training
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Jones N, Kiely J, Suraci B, et al. A response to letter to the editor: A genetic-based algorithm for personalized resistance training. Biology of Sport. 2017;34(1):35-37. doi:10.5114/biolsport.2017.63386. APA Jones, N., Kiely, J., Suraci, B., Collins, D., de Lorenzo, D., & Pickering, C. et al. (2017). A response to letter to the editor: A genetic-based algorithm for personalized resistance training. Biology of Sport, 34(1), 35-37. https://doi.org/10.5114/biolsport.2017.63386 Chicago Jones, N, J Kiely, B Suraci, DJ Collins, D de Lorenzo, C Pickering, and KA Grimaldi. 2017. "A response to letter to the editor: A genetic-based algorithm for personalized resistance training". Biology of Sport 34 (1): 35-37. doi:10.5114/biolsport.2017.63386. Harvard Jones, N., Kiely, J., Suraci, B., Collins, D., de Lorenzo, D., Pickering, C., and Grimaldi, K. (2017). A response to letter to the editor: A genetic-based algorithm for personalized resistance training. Biology of Sport, 34(1), pp.35-37. https://doi.org/10.5114/biolsport.2017.63386 MLA Jones, N et al. "A response to letter to the editor: A genetic-based algorithm for personalized resistance training." Biology of Sport, vol. 34, no. 1, 2017, pp. 35-37. doi:10.5114/biolsport.2017.63386. Vancouver Jones N, Kiely J, Suraci B, Collins D, de Lorenzo D, Pickering C et al. A response to letter to the editor: A genetic-based algorithm for personalized resistance training. Biology of Sport. 2017;34(1):35-37. doi:10.5114/biolsport.2017.63386.
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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 | 0.006 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".