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Record W1686736921 · doi:10.1111/jfb.12527

Novel life‐history data for threatened seahorses provide insight into fishery effects

2014· article· en· W1686736921 on OpenAlexaff
Julia M. Lawson, Sarah J. Foster, Adam Chee Ooi Lim, Ving Ching Chong, Amanda C. J. Vincent

Bibliographic record

VenueJournal of Fish Biology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsUniversity of British Columbia
FundersExplorers Club
KeywordsSeahorseBiologyFisheryPeninsulaThreatened speciesZoologyEcologyHabitat

Abstract

fetched live from OpenAlex

Life-history variables for three incidentally captured species of seahorse (Kellogg's seahorse Hippocampus kelloggi, the hedgehog seahorse Hippocampus spinosissimus and the three-spot seahorse Hippocampus trimaculatus) were established using specimens obtained from 33 fisheries landing sites in Peninsular Malaysia. When samples were pooled by species across the peninsula, sex ratios were not significantly different from unity, and height and mass relationships were significant for all species. For two of these species, height at physical maturity (HM ) was smaller than the height at which reproductive activity (HR ) commenced: H. spinosissimus (HM = 99·6 mm, HR = 123·2 mm) and H. trimaculatus (HM = 90·5 mm, HR = 121·8 mm). For H. kelloggi, HM could not be estimated as all individuals were physically mature, while HR = 167·4 mm. It appears that all three Hippocampus spp. were, on average, caught before reproducing; height at 50% capture (HC ) was ≥HM but ≤HR . The results from this study probe the effectiveness of assessment techniques for data-poor fisheries that rely heavily on estimates of length at maturity, especially if maturity is poorly defined. Findings also question the sustainability of H. trimaculatus catches in the south-west region of Peninsular Malaysia, where landed specimens had a notably smaller mean height (86·2 mm) and markedly skewed sex ratio (6% males) compared with samples from the south-east and north-west of the peninsula.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.175

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 head0.071
GPT teacher head0.254
Teacher spread0.183 · 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 designNot applicable
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

Citations20
Published2014
Admission routes1
Has abstractyes

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