Comment on “Purported flaws in management strategy evaluation: basic problems or misinterpretation?” by Butterworth et al.
Bibliographic record
Abstract
Abstract Rochet, M-J., and Rice, J. C. 2010. Comment on “Purported flaws in management strategy evaluation: basic problems or misinterpretation?” by Butterworth et al. – ICES Journal of Marine Science, 67: 575–576. Simulation-based management strategy evaluation is a valuable tool, when appropriately implemented. Implementation, however, may not always have been appropriate, and some reasons are provided why perhaps there is incomplete faith in certain of its technical aspects, such as knowing the distribution of the parameters of population processes from the information in limited datasets. A management strategy that has been evaluated by simulation should not be used as an “autopilot”, because even the most competent of experts can develop autopilots with imperfect and incomplete knowledge of reality, and all information should be incorporated when decisions have to be made.
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.
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 | 0.024 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.072 | 0.063 |
| Insufficient payload (model declined to judge) | 0.013 | 0.016 |
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".