Sidney Holt, a giant in the history of fisheries science who focused on the future: his legacy and challenges for present-day marine scientists
Bibliographic record
Abstract
Abstract Sidney J. Holt (1926–2019) was more than a founding father of quantitative fisheries science, and the man who “helped save the great whales.” His accomplishments, over a career spanning seven decades, run deeper: he was a champion of reductionism (i.e. able to identify the factors essential for management) and a systemic thinker who inspired scientists to think critically about marine conservation and management. This article draws on first-hand experiences with Sidney over the last 15 years, when he regularly collaborated with scholars of the ICES Working Group on the History of Fish and Fisheries and the Oceans Past Initiative. Four main themes emerged from our reflections on Sidney’s life and legacy, which constitute ongoing scientific challenges: (1) the suitability of maximum sustainable yield as a target reference point for fisheries management; (2) the future of marine mammal conservation; (3) successful implementation of ecosystem-based marine management; and (4) the value of historical perspectives for conservation and management. We consider Sidney’s work across these themes, in which he readily collaborated, focused on evidence-based solutions, and, where evidence was lacking, he advocated for the “precautionary principle.” We posit there is much that we, and future generations of scientists, can learn from his example.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".