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Record W3173691300 · doi:10.1139/facets-2020-0038

Identifying and monitoring of forage fish spawning beaches in British Columbia’s Salish Sea for conservation of forage fish

2021· article· en· W3173691300 on OpenAlexaffvenueabout
Haley Tomlin, Chrissy Schellenberg, Jacklyn B. Barrs, Alanna J.S. Vivani, Pam Shaw

Bibliographic record

VenueFACETS · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsWorld Wildlife Fund CanadaVancouver Island University
Fundersnot available
KeywordsSmeltForage fishBayFisheryHabitatGeographyForageOceanographyFish <Actinopterygii>EcologyGeologyBiologyArchaeology

Abstract

fetched live from OpenAlex

Pacific sand lance ( Ammodytes personatus) and surf smelt ( Hypomesus pretiosus) are ecologically important forage fish in the marine food webs within the Salish Sea, including British Columbia (BC). Although important, little information exists around the spatiotemporal distribution of these fishes’ beach spawning habitat in the BC Salish Sea. In 2017, the Mount Arrowsmith Biosphere Region Research Institute initiated spawning beach surveys within the Mount Arrowsmith Biosphere Region (MABR). Surveys have geographically expanded along the eastern Vancouver Island coastline between Bowser and Cowichan Bay, BC, including Gabriola Island and Thetis Island. Pacific sand lance embryos have been detected at 17 beaches, with six beaches located within the MABR. Pacific sand lance spawning events have been detected between November and mid-February, with the bulk of embryos detected in November and December. To date, surf smelt embryos have not been detected at the 56 different beaches. These data begin to fill existing data gaps surrounding Pacific sand lance and surf smelt in BC. Furthermore, longer-term data submissions to the Strait of Georgia Data Centre, an open-access database, will provide the necessary data needed to advocate for improved regulatory protections for these ecologically important fish and their spawning habitat.

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 score0.096
Threshold uncertainty score0.983

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 head0.037
GPT teacher head0.273
Teacher spread0.236 · 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

Citations5
Published2021
Admission routes3
Has abstractyes

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