Herring and ICES: a historical sketch of a few ideas and their linkages
Bibliographic record
Abstract
Abstract Sinclair, M. 2009. Herring and ICES: a historical sketch of a few ideas and their linkages. – ICES Journal of Marine Science, 66: 1652–1661. This introduction to the Symposium on “Linking Herring” sketches the development of some ideas generated from herring research within an ICES context. The work of Committee A (1902–1908), under the leadership of Johan Hjort, led to a paradigm shift from “migration thinking” to “population thinking” as the interpretation of fluctuations in herring landings. From the 1920s to the 1950s, the focus on forecasting services for the herring fisheries, although ultimately unsuccessful, had the unintended consequence of generating ideas on recruitment overfishing and the match–mismatch hypothesis. The collapse of the East Anglian fishery led, in 1956, to considerable debate on its causes, but no consensus was reached. Three consecutive symposia dealing with herring (1961, 1968, and 1970) reveal a changing perspective on the role of fishing on recruitment dynamics, culminating in Cushing’s 1975 book (“Marine Ecology and Fisheries”, referred to here as the “Grand Synthesis”), which defined the concept of recruitment overfishing and established the future agenda for fisheries oceanography. The 1978 ICES “Symposium on the Assessment and Management of Pelagic Fish Stocks” is interpreted as the “Aberdeen Consensus” (i.e. without effective management, recruitment overfishing is to be expected). In conclusion, herring research within ICES has led to many ideas and two major paradigm shifts.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".