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Record W4391852327 · doi:10.3354/esr01318

Past and present sawfish (Pristidae) records from India

2024· article· en· W4391852327 on OpenAlexaff
Zoya Tyabji, Rima W. Jabado, K Akhilesh, SJ Kizhakudan, M. Aaron MacNeil

Bibliographic record

VenueEndangered Species Research · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFish Biology and Ecology Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGeographyZoologyFisheryBiology

Abstract

fetched live from OpenAlex

Overfishing, as well as habitat loss and degradation, has led to major population declines and local extinctions of sawfishes (Pristidae) globally. Four sawfish species reportedly occur in India; however, records have been limited to opportunistic commercial catch and landing reports. Here, we provide the first comprehensive review of published and grey literature on sawfish records from India, including opportunistic observations of sawfish rostra offerings to religious places, highlighting the cultural significance of these species locally. In total, 223 recorded capture events were compiled between 1794 and 2022, with largetooth sawfish Pristis pristis (n = 82), followed by narrow sawfish Anoxypristis cuspidata (n = 32), being the dominant species reported. In addition to marine fisheries, 8 reports of sawfish were reported from freshwater systems. The wide range of rostra sizes and total lengths recorded also suggests that India’s waters harbour various life history stages of sawfish. When caught, sawfish livers were utilised to produce oil, meat was locally consumed, and fins were exported. Despite being legally protected in India since 2001, 63 incidental captures were recorded from landings, suggesting various levels of awareness and enforcement of catch bans across the country. To avoid extinction of these species in India, we emphasise the need to conduct culturally associated awareness programs with coastal communities, encourage safe release and improve handling practices with fishers, identify critical habitats, and strengthen enforcement for mandatory live release.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.304
Teacher spread0.231 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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