Bibliographic record
Abstract
Abstract This paper describes my research on fish population dynamics, which has aimed to improve the information available for management and conservation. Through numerous collaborations, my research program addressed three main objectives. (1) Increase the understanding of spatial and temporal variation in productivity of fish populations. (2) Quantify uncertainties and risks in fishery systems and their implications for management and conservation. (3) Develop methods to reduce those uncertainties and risks. To help young scientists, I present 11 general lessons, as well as some specific advice, that emerged from that research. The general lessons include pursuing a path of continuous learning, going beyond your comfort zone to broaden your skills and knowledge, and collaborating with others. More specific advice for fisheries scientists includes evaluating the bias and precision of parameter estimation methods via Monte Carlo simulations, and considering multiple models of whole fishery systems. This paper also illustrates, with examples, how the understanding of some aspects of fish population dynamics has evolved, at least from the limited perspective of my own group's research.
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 | 0.034 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".