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Record W2053326556 · doi:10.1080/02755947.2012.663455

Growth Rate of the California Sea Cucumber <i>Parastichopus californicus</i>: Measurement Accuracy and Relationships between Size and Weight Metrics

2012· article· en· W2053326556 on OpenAlexafffund
Lucie Hannah, Nicholas Duprey, John Blackburn, Claudia M. Hand, Christopher M. Pearce

Bibliographic record

VenueNorth American Journal of Fisheries Management · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEchinoderm biology and ecology
Canadian institutionsFisheries and Oceans Canada
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsBayFisheryJuvenileSea cucumberBiologyCondition indexEnvironmental scienceAnimal scienceOceanographyEcology

Abstract

fetched live from OpenAlex

Abstract Management of the fishery for California sea cucumbers Parastichopus californicus in the Pacific Northwest is limited by a lack of natural growth rate estimates. Growth rates of caged juvenile California sea cucumbers (from Departure Bay, British Columbia) consuming a natural diet for 12 months (September 2008–September 2009) were examined. Growth was low between September and March but significantly increased thereafter, appearing to follow seasonal physiological processes, temperature, and natural sedimentation rates. Over the 12-month period, whole wet weight in air (WWA) increased by an average of 164%, immersed whole weight (IWW) increased by 251%, and the size index (SI) increased by 85%. Average standard growth rates were 0.267%/d for WWA, 0.346%/d for IWW, and 0.169%/d for SI. Measurement accuracy, effects of body content, and relationships between size and weight metrics are discussed. These findings are an important addition to the knowledge of California sea cucumber biology and are valuable for the stock assessment, fisheries management, and aquaculture of this species. Received January 1, 2011; accepted November 10, 2011

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 score0.014
Threshold uncertainty score0.212

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 studies0.0000.000
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 head0.025
GPT teacher head0.201
Teacher spread0.177 · 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

Citations31
Published2012
Admission routes2
Has abstractyes

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