A response to letter to the editor: A genetic-based algorithm for personalized resistance training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it