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Record W2157689568 · doi:10.2983/035.029.0403

Validation of a Noninvasive Technique for Estimating Fecundity in the American Lobster<i>Homarus americanus</i>

2010· article· en· W2157689568 on OpenAlexafffund
Jens J. Currie, David C. Schneider, Kate Wilke

Bibliographic record

VenueJournal of Shellfish Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFecundityHomarusBiologyAmerican lobsterNotchingSampling (signal processing)FisheryEcologyZoologyCrustaceanPopulationEngineering

Abstract

fetched live from OpenAlex

We investigated a noninvasive sampling technique that estimates fecundity for ovigerous American lobster based on field measurement and live release. These estimates were compared with fecundity estimates obtained from the widely used traditional invasive technique involving the removal, drying, and weighing of the entire egg mass. The noninvasive technique, which requires the removal of only 10 eggs per female, produced fecundity estimates that were within 4% of those obtained using the traditional invasive method. Applications of this technique may be carried out in an experimental setting where the effects of conservation measures such as v-notching or the establishment of closed areas, aimed at increasing egg production, can be quantified without the use of destructive sampling techniques.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.365
Teacher spread0.322 · 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

Citations13
Published2010
Admission routes2
Has abstractyes

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