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Record W213329171 · doi:10.2166/wqrj.2002.012

Non-Lethal Sampling Methods for Assessing Environmental Impacts Using a Small-Bodied Sentinel Fish Species

2002· article· en· W213329171 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueWater Quality Research Journal · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of New Brunswick
FundersCanada Research Chairs
KeywordsSampling (signal processing)FisherySculpinFish <Actinopterygii>Environmental scienceEnvironmental monitoringEcologyEnvironmental resource managementBiologyEngineering

Abstract

fetched live from OpenAlex

Abstract Under the Canadian Fisheries Act, pulp and paper mills and metal mines must conduct a cyclical monitoring program for potential environmental effects that includes a fish survey. Study designs for the fish survey have been evolving over the past few years, and there has been increased emphasis on the use of small-bodied fish species. Increasing concerns about the potential impacts of sampling programs on the fish populations in smaller receiving waters have led us to develop non-lethal sampling methodologies that will satisfy the information requirements for the environmental effects monitoring program. This manuscript outlines the use of a non-lethal sampling program to collect information on age distributions, growth rates, reproductive performance and fish condition in populations of slimy sculpin inhabiting forested and agricultural sections of a small New Brunswick river.

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.

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.387
GPT teacher head0.480
Teacher spread0.093 · 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