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Record W4415648089 · doi:10.3390/fishes10110544

Using Citizen Science to Improve Our Understanding of Northern Shortfin Squid (Illex illecebrosus) and Longfin Inshore Squid (Doryteuthis pealeii) Ecology and Fisheries off Atlantic Canada

2025· article· en· W4415648089 on OpenAlexaffabout
Krista D. Baker, Chelsea Malayny, Neil Ollerhead

Bibliographic record

VenueFishes · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsSquidFishingRecreational fishingFisheries managementRecreationBycatch

Abstract

fetched live from OpenAlex

Northern shortfin squid (Illex illecebrosus) and longfin inshore squid (Doryteuthis pealeii) are fished commercially and recreationally off Atlantic Canada, but limited reporting, particularly from recreational fisheries, has left major gaps in our understanding of their ecology and fishery dynamics. Our research used three years of data collected through citizen science initiatives, field visits, and commercial index harvesters to provide much-needed descriptions of squid fisheries and basic ecological characteristics, with a particular focus on Newfoundland and Labrador (NL). We documented active recreational squid fisheries landing both species, including the first confirmed observations of longfin inshore squid in the NL fishery. Distinct regional patterns emerged. North Coast Region fishers tended to use different gear and practices, achieved higher catch-per-unit effort (CPUE), and caught larger squid compared to other NL regions. South Coast Region fishers more frequently caught longfin inshore squid, and although relatively low CPUE was reported, this region tended to have a more active recreational fishery. Our findings reveal previously unrecognized regional variation in squid fisheries and highlight the need for improved understanding of squid ecology and the impacts of the recreational and commercial fisheries on the resource off Atlantic Canada.

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)0.000
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 score0.685
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.027
GPT teacher head0.235
Teacher spread0.209 · 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 routes2
Has abstractyes

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