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Record W2075698732 · doi:10.3354/meps09738

Trophic-level determinants of biomass accumulation in marine ecosystems

2012· article· en· W2075698732 on OpenAlexafffund
Fabio Pranovi, Jason S. Link, Caihong Fu, AM Cook, H Liu, Sarah Gaichas, KD Friedland, K Rong Utne, HP Benoît

Bibliographic record

VenueMarine Ecology Progress Series · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans CanadaVancouver Island University
FundersFisheries and Oceans CanadaNational Marine Fisheries ServiceNorges ForskningsrådHavforskningsinstituttet
KeywordsFisheries scienceTrophic levelMarine ecosystemFisheryFisheries managementFood webGeographyEcosystem servicesEcosystemBiomass (ecology)OceanographyEcologyFishingBiology

Abstract

fetched live from OpenAlex

Metrics representative of key ecosystem processes are required for monitoring and understanding system dynamics, as a function of ecosystem-based fisheries management (EBFM). Useful properties of such indicators should include the ability to capture the range of variation in ecosystem responses to a range of pressures, including anthropogenic (e.g. exploitation pressures) and environmental (e.g. climate pressures), as well as indirect effects (e.g. those related to food web processes). Examining modifications in ecological processes induced by structural changes, however, requires caution because of the inherent uncertainty, long feedback times, and highly nonlinear ecosystem responses to external perturbations. Yet trophodynamic indicators are able to capture important changes in marine ecosystem function as community structures have been altered. One promising family of such metrics explores the changing biomass accumulation in the middle trophic levels (TLs) of marine ecosystems. Here we compared cumulative biomass curves across TLs for a range of northern hemisphere temperate and boreal ecosystems. Our results confirm that sigmoidal patterns are consistent across different ecosystems and, on a broad scale, can be used to detect factors that most influence shifts in the cumulative biomass-TL curves. We conclude that the sigmoidal relationship of biomass accumulation curves over TLs could be another possible indicator useful for the implementation of EBFM.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.991

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 communication0.0000.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.042
GPT teacher head0.303
Teacher spread0.261 · 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

Citations18
Published2012
Admission routes2
Has abstractyes

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