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Record W2298992075 · doi:10.1080/02705060.2015.1123655

Seasonal habitat use of brook trout and juvenile steelhead in a Lake Ontario tributary

2016· article· en· W2298992075 on OpenAlexaboutno aff
James H. Johnson, Ross Abbett, Marc A. Chalupnicki, Francis Verdoliva

Bibliographic record

VenueJournal of Freshwater Ecology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersU.S. Geological Survey
KeywordsTroutSalvelinusTributaryFontinalisHabitatFisheryJuvenileEcologyInterspecific competitionRainbow troutOncorhynchusBiologyGeographyEnvironmental scienceFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Brook trout (Salvelinus fontinalis) are generally restricted to headwaters in New York tributaries of Lake Ontario. In only a few streams are brook trout abundant in lower stream reaches that are accessible to adult Pacific salmonids migrating from the lake. Consequently, because of the rarity of native brook trout populations in these lower stream reaches it is important to understand how they use stream habitat in sympatry with juvenile Pacific salmonids which are now naturalized in several Lake Ontario tributaries. In this study, we examined the seasonal (spring, summer, and fall) habitat use of brook trout and juvenile steelhead (Oncorhynchus mykiss) in Hart Brook, a tributary of eastern Lake Ontario. We found interspecific, intraspecific, and seasonal variation in habitat use. Subyearling steelhead were associated with faster water velocities than subyearling brook trout and, overall, had the least habitat similarity to the other salmonid groups examined. Overyearling brook trout and yearling steelhead exhibited the greatest degree of habitat selection and habitat selection by all four salmonid groups was greatest in summer. The availability of pool habitat for overyearling salmonids may pose the largest impediment to these species in Hart Brook.

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 categoriesInsufficient 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 score0.936
Threshold uncertainty score0.998

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)0.0030.000

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 head0.013
GPT teacher head0.205
Teacher spread0.192 · 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.

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

Citations4
Published2016
Admission routes1
Has abstractyes

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