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Record W2416332110 · doi:10.1139/cjfas-2015-0485

The contribution of fecundity and embryo quality to reproductive potential of eastern Bering Sea snow crab (<i>Chionoecetes opilio</i>)

2016· article· en· W2416332110 on OpenAlexvenueaboutno aff
Joel B. Webb, Laura M. Slater, Ginny L. Eckert, Gordon H. Kruse

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsnot available
FundersNorth Pacific Research BoardAlaska Department of Fish and GameMassachusetts Department of Fish and Game
KeywordsFecundityBiologyZoologyEcologyFisheryPopulationDemography

Abstract

fetched live from OpenAlex

Development of refined indices of female reproductive potential is needed for estimation of alternative biological reference points for the eastern Bering Sea (EBS) snow crab (Chionoecetes opilio) fishery, which is managed with large male-only harvest regulations. Females were collected from 2007 to 2009 to investigate seasonal and interannual variation in fecundity with maternal size, shell condition (a proxy for age after maturity), and recent mating and again in 2010 to examine biochemical measures (carbon, hydrogen, and nitrogen) of embryo quality. Mean model-adjusted fecundity was highest for primiparous and young multiparous females and declined with advancing shell condition, presumably from senescence. This pattern was also found for clutch fullness indices evaluated from 20 years of stock assessment survey data. Indicators of low female sperm reserves were associated with decreased (∼10%) fecundity for multiparous females. Seasonal comparison of size–fecundity relationships suggested that embryo loss during brooding was minimal, and embryo quality analyses suggested that strong variation with maternal characteristics was unlikely. Finally, fecundity-at-size of EBS females may be lower than that of conspecifics in Japan and eastern Canada.

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)0.001
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 score0.036
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.014
GPT teacher head0.232
Teacher spread0.217 · 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

Citations11
Published2016
Admission routes2
Has abstractyes

Explore more

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