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Record W2144821682 · doi:10.1098/rsbl.2010.1213

Comparative analysis of European wide marine ecosystem shifts: a large-scale approach for developing the basis for ecosystem-based management

2011· article· en· W2144821682 on OpenAlexaboutno aff
Christian Möllmann, Alessandra Conversi, Martin Edwards

Bibliographic record

VenueBiology Letters · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersNatural Environment Research Council
KeywordsMarine ecosystemEcosystemEcosystem-based managementFisheries scienceFisheryOceanographyMediterranean seaEcosystem managementEcologyMarine conservationFisheries managementEnvironmental resource managementBiologyMediterranean climateEnvironmental scienceFishing

Abstract

fetched live from OpenAlex

Abrupt and rapid ecosystem shifts (where major reorganizations of food-web and community structures occur), commonly termed regime shifts, are changes between contrasting and persisting states of ecosystem structure and function. These shifts have been increasingly reported for exploited marine ecosystems around the world from the North Pacific to the North Atlantic. Understanding the drivers and mechanisms leading to marine ecosystem shifts is crucial in developing adaptive management strategies to achieve sustainable exploitation of marine ecosystems. An international workshop on a comparative approach to analysing these marine ecosystem shifts was held at Hamburg University, Institute for Hydrobiology and Fisheries Science, Germany on 1-3 November 2010. Twenty-seven scientists from 14 countries attended the meeting, representing specialists from seven marine regions, including the Baltic Sea, the North Sea, the Barents Sea, the Black Sea, the Mediterranean Sea, the Bay of Biscay and the Scotian Shelf off the Canadian East coast. The goal of the workshop was to conduct the first large-scale comparison of marine ecosystem regime shifts across multiple regional areas, in order to support the development of ecosystem-based management strategies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.711
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.257
Teacher spread0.214 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations20
Published2011
Admission routes1
Has abstractyes

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