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Record W7104288895 · doi:10.14286/6ezvgv

Juvenile striped bass overwintering behaviour

2023· dataset· en· W7104288895 on OpenAlexaff

Bibliographic record

VenueOcean Tracking Network · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsOcean Tracking Network
Fundersnot available
KeywordsOverwinteringBass (fish)JuvenileSatellite trackingStructural basin

Abstract

fetched live from OpenAlex

This is the OBIS extraction of the Ocean Tracking Network and Acadia University (Acadia U) Juvenile striped bass overwintering behaviour, consisting of the release tagging metadata, i.e. the location and date when the tagged animal was released, and summarized detection events of tagged individuals. If readers are interested in the source dataset they may also inquire with the project PIs as listed here or on the OTN web site (https://members.oceantrack.org/project?ccode=ALLIMB).Abstract:Acoustic receivers have been deployed throughout the Minas Basin. Seventy-five striped bass were tagged with Innovasea acoustic tags at various locations around the Minas Basin in Fall 2023, and at least 20 more will be tagged in Fall 2024. This project's objectives are to identify juvenile striped bass overwintering habitat(s) in the Minas Basin and quantify juvenile striped bass over winterer use of the Minas Passage using acoustic telemetry. This research is part of a collaborative project between Mi'kmaw traditional knowledge holders, fishers, academia, community partners, and government. The results from this research will assess the risk of striped bass collisions with tidal energy infrastructure in the Minas Passage during an important life stage and fill knowledge gaps important for their management and conservation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.013

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 head0.031
GPT teacher head0.286
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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