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Record W2752122142 · doi:10.1080/02705060.2017.1344734

Monitoring lake populations of Eastern Sand Darter (<i>Ammocrypta pellucida</i>): a comparison of two seines

2017· article· en· W2752122142 on OpenAlexafffundabout
Scott M. Reid, Alan J. Dextrase

Bibliographic record

VenueJournal of Freshwater Ecology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsTrent UniversityMinistry of Natural Resources and Forestry
FundersFisheries and Oceans CanadaTrent UniversityUniversity of CalgaryOntario Ministry of Natural Resources and ForestryMinistry of Natural Resources
KeywordsThreatened speciesElectrofishingAbundance (ecology)GeographyEcologyPopulationFisheryEnvironmental scienceSampling (signal processing)BiologyHabitat

Abstract

fetched live from OpenAlex

For many imperiled fishes in the Laurentian Great Lakes basin, detection protocols and population monitoring programs are lacking. In this study, we used a repeat-sampling approach to compare the effectiveness of two seines (bag and beach) to detect and characterize the abundance of lake-dwelling populations of Eastern Sand Darter (Ammocrypta pellucida); a threatened species in Canada. Compared to the bag seine, the larger beach seine collected a greater number of Eastern Sand Darter and detected the species at more sampling sites. Model-averaged estimates of detection probability were also greater for the beach seine (p = 0.72) than bag seine (p = 0.48). A decline in catch over repeated seine hauls occurred at less than a third of the sample units. Mean capture probabilities were 0.41 in units sampled by beach seine, and 0.37 in units sampled by bag seine, when depletion occurred. Sizes of Eastern Sand Darter collected by each seine were significantly different, with fewer small (<40 mm total length) individuals found in bag seine hauls. Power analyses indicate that data collected with either seine are expected to detect changes in local distribution of 50% or greater. Power to detect future changes in Eastern Sand Darter abundance of 50% or greater is predicted to be higher for the beach seine, and to increase with the number of seine hauls at a site. Catch differences between seines are interpreted to reflect the greater area sampled by the larger beach seine.

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 score0.032
Threshold uncertainty score0.986

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)0.0010.000

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 head0.043
GPT teacher head0.319
Teacher spread0.276 · 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

Citations6
Published2017
Admission routes3
Has abstractyes

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