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Record W4413446846 · doi:10.1093/jcbiol/ruaf046

Changes in v-notch depth after molting in the American lobster, <i>Homarus americanus</i> Milne-Edwards, 1837 (Decapoda: Pleocyemata: Nephropidae)

2025· article· en· W4413446846 on OpenAlexafffundabout
Krista D. Baker, Darrell Mullowney, Elizabeth Coughlan

Bibliographic record

VenueJournal of Crustacean Biology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsHomarusBiologyAmerican lobsterDecapodaMoultingZoologyCrustaceanFisheryAnatomyEcologyLarva

Abstract

fetched live from OpenAlex

Abstract The stocks of the American lobster, Homarus americanus Milne-Edwards, 1837, off Newfoundland and Labrador, Canada are heavily exploited and few females survive beyond their first molt into the fishery. V-notching is a mark-recapture, voluntary practice in the fishery but there are strict guidelines on the retention of any possible v-notched lobsters. In response to calls to relax the definition of what constitutes a v-notch, we developed a laboratory study to quantify the change in v-notch depth with molt and relate changes in carapace length to changes in potential fecundity. We collected 32 egg-bearing lobster from the field in June 2022. The females were v-notched, and the v-notch depth and carapace length were measured before and after molting. We found the v-notches were clearly visible after one molt. Under current restrictions, these females would have been protected during their following clutch, where potential fecundity increased by up to 68%; however, 18.5–88.9% could have been harvested if the definition of v-notched was relaxed. These findings suggest that current restrictions are more effective in the management of this heavily exploited fishery, and more widespread v-notching would boost the population’s reproductive potential and survival rate of newly recruited females.

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 score0.077
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.271
Teacher spread0.262 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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