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Record W1969286628 · doi:10.1093/icesjms/fsp115

Herring and ICES: a historical sketch of a few ideas and their linkages

2009· article· en· W1969286628 on OpenAlexaff
Mike Sinclair

Bibliographic record

VenueICES Journal of Marine Science · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsBedford Institute of Oceanography
Fundersnot available
KeywordsOverfishingHerringFishingSketchFisheryPopulationFisheries managementPelagic zoneContext (archaeology)GeographyFish <Actinopterygii>SociologyBiologyComputer scienceArchaeology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0040.015
Scholarly communication0.0080.011
Open science0.0010.003
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.014
GPT teacher head0.252
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2009
Admission routes1
Has abstractyes

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