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Record W4401631496 · doi:10.1139/cjfas-2023-0234

An integrative approach to assessing bridle shiner (<i>Notropis bifrenatus</i>) distribution using environmental DNA and traditional techniques

2024· article· en· W4401631496 on OpenAlexvenueaboutno aff
Lara S. Katz, Stephen M. Coghlan, Erik J. Blomberg, Michael T. Kinnison, Geneva York, Joseph Zydlewski

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsnot available
FundersMaine Department of Inland Fisheries and Wildlife
KeywordsNotropisEnvironmental DNAEcologyBiologyFisheryZoologyEvolutionary biologyFish <Actinopterygii>Biodiversity

Abstract

fetched live from OpenAlex

The bridle shiner ( Notropis bifrenatus) is a small cyprinid native to the eastern United States and Canada. Bridle shiner populations have declined across their range, and the species now receives concern status or legal protection in 13 states and two provinces. Bridle shiners were historically found in southern and western Maine in densely vegetated, shallow habitats along the shorelines of streams and ponds. We surveyed areas of Maine that supported historical bridle shiner populations using environmental DNA (eDNA) and traditional seine netting methods, and then used eDNA sampling to survey areas with unknown bridle shiner presence. We rediscovered bridle shiner populations at 11 of 32 historically occupied waterbodies and documented bridle shiners in four additional waterbodies. We determined that both eDNA and seine net surveys are viable options for monitoring bridle shiners in Maine and identified ways to streamline the eDNA methods used in this study to reduce the time and cost of future surveys.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.225
Teacher spread0.199 · 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
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 routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicEnvironmental DNA in Biodiversity StudiesFrench-language works237,207