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Record W1993523754 · doi:10.1098/rsbl.2010.0526

Comparative analysis of marine ecosystems: international production modelling workshop

2010· article· en· W1993523754 on OpenAlexaff
Jason S. Link, Bernard A. Megrey, Thomas J. Miller, Tim Essington, Jennifer L. Boldt, Alida Bundy, Erlend Moksness, Ken Drinkwater, R. Ian Perry

Bibliographic record

VenueBiology Letters · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsEcosystemGuildMarine ecosystemBiomass (ecology)ProductivityEnvironmental resource managementBiologyMarine conservationProduction (economics)FisheryEcologyEnvironmental scienceHabitat

Abstract

fetched live from OpenAlex

Understanding the drivers that dictate the productivity of marine ecosystems continues to be a globally important issue. A vast literature identifies three main processes that regulate the production dynamics of such ecosystems: biophysical, exploitative and trophodynamic. Exploring the prominence among this 'triad' of drivers, through a synthetic analysis, is critical for understanding how marine ecosystems function and subsequently produce fisheries resources of interest to humans. To explore this topic further, an international workshop was held on 10-14 May 2010, at the National Academy of Science's Jonsson Center in Woods Hole, MA, USA. The workshop compiled the data required to develop production models at different hierarchical levels (e.g. species, guild, ecosystem) for many of the major Northern Hemisphere marine ecosystems that have supported notable fisheries. Analyses focused on comparable total system biomass production, functionally equivalent species production, or simulation studies for 11 different marine fishery ecosystems. Workshop activities also led to new analytical tools. Preliminary results suggested common patterns driving overall fisheries production in these ecosystems, but also highlighted variation in the relative importance of each among ecosystems.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0060.000

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 head0.030
GPT teacher head0.285
Teacher spread0.255 · 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.

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

Citations23
Published2010
Admission routes1
Has abstractyes

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