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Record W2060930016 · doi:10.1093/icesjms/fsn035

Coexistence in North Sea fish communities: implications for growth and natural mortality

2008· article· en· W2060930016 on OpenAlexaff
Henrik Gislason, John G. Pope, Jake Rice, N. Daan

Bibliographic record

VenueICES Journal of Marine Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans Canada
FundersDanmarks Tekniske Universitet
KeywordsPelagic zoneDemersal zoneDemersal fishScalingFisheryBiomass (ecology)Stock assessmentEcologyBiologyFish <Actinopterygii>Environmental scienceGeographyFishingMathematics

Abstract

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Abstract Gislason, H., Pope, J. G., Rice, J. C., and Daan, N. 2008. Coexistence in North Sea fish communities: implications for growth and natural mortality. – ICES Journal of Marine Science, 65: 514–530. For a fish community to persist over time, all species must be able on average to replace themselves on a one-for-one basis over their lifetime. We use this principle and a size-based equilibrium model where asymptotic length is used as a functional trait to investigate how natural mortality should scale with size within and across pelagic and demersal species of North Sea teleosts. The model predicts natural mortality to scale with body length raised to a power of −1.66 at current levels of exploitation. Additionally, natural mortality of demersal species should be proportional to asymptotic length raised to a power of 0.80, so generating a higher natural mortality at a given length for large species than for small ones. The model also suggests that the exponent in the scaling of the von Bertalanffy growth parameter K with asymptotic length should be more negative for pelagic than for demersal species. We test our results by analysing independent estimates of predation mortality, the scaling of maximum recruitment per unit of spawning-stock biomass with asymptotic length, and the general relationship between K and asymptotic length for demersal and pelagic families of fish. All tests are consistent with our modelling results.

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: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.047
GPT teacher head0.306
Teacher spread0.260 · 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

Citations60
Published2008
Admission routes1
Has abstractyes

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