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Record W2534632115 · doi:10.3390/d8040022

Ecology of the River Darter in Canadian Waters: Distribution, Relative Abundance, Life-History Traits, Diet, and Habitat Characteristics

2016· article· en· W2534632115 on OpenAlexaffabout
Thomas C. Pratt, William M. Gardner, Douglas A. Watkinson, Lynn D. Bouvier

Bibliographic record

VenueDiversity · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsEcoregionBayEcologyHabitatDominance (genetics)Range (aeronautics)Abundance (ecology)GeographyRelative species abundanceBiologyFisheryArchaeology

Abstract

fetched live from OpenAlex

The River Darter (Percina shumardi) is a native, rarely sampled fish that has been detected in relatively small numbers since the 1930s. It has a wide North American distribution, but little is known about the species biology and distribution across the Canadian portion of its range. We revisited many historic sampling locations and sampled additional areas to (i) confirm and update the distribution of River Darters in Canadian waters; (ii) assess relative abundance; (iii) update life history characteristics; (iv) collect diet information; and (v) identify characteristics of River Darter habitat. Since 1990, a total of 1032 River Darters were recorded from 29 waterbodies across three ecoregions. River Darters were observed in relatively high abundances in both the Saskatchewan-Nelson River and Southern Hudson Bay-James Bay ecoregions. While still extant in the Great Lakes-Upper St. Lawrence ecoregion, River Darters persist at low abundance. Life history analyses indicate similar maximum age (age 4) and growth (10 mm/year) to conspecific populations in the United States, while sex ratios are generally skewed towards female dominance. River Darter populations had high flexibility in habitat use and diet, using a range of flows and depths and a variety of seasonally available prey types.

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 score0.315
Threshold uncertainty score0.994

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 studies0.0000.001
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 head0.007
GPT teacher head0.158
Teacher spread0.150 · 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

Citations3
Published2016
Admission routes2
Has abstractyes

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