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Summer use of a small stream by fish and crayfish and exchanges with adjacent lentic macrohabitats

2004· article· en· W2102040390 on OpenAlexaff
Claudia Cossette, Marco A. Rodríguez

Bibliographic record

VenueFreshwater Biology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsCrayfishBeaverBiologyEcologyMinnowTributaryWater levelBiological dispersalFisheryFish <Actinopterygii>GeographyPopulationDemography

Abstract

fetched live from OpenAlex

Summary 1. Movements between a stream reach and two adjacent lentic macrohabitats, a beaver pond and a lake, were followed for the Appalachian crayfish and two fish species, brook charr and brown bullhead, over an 85‐d period from early June to late August, and were analysed in relation to water level, maximum water temperature, photoperiod length, lunar luminosity, and age, by use of time‐series regressions. 2. Brook charr showed strong net immigration to the stream reach for underyearling (age class 0+) fish but net emigration for 1+ fish. Both immigration and emigration were positively related to water level and temperature; migratory responses to temperature were age‐specific. 3. Brown bullhead used the stream primarily as a corridor for downstream migration from the beaver pond to the lake. As with brook charr, water level and temperature had a positive effect on movement and responses were stronger in younger individuals. 4. Crayfish emigrated from the stream during the summer. Movements were positively related to increases in water level and temperature, with responses of 1+ crayfish much stronger than those of older individuals. 5. The results indicate that the stream tributary played different roles for brook charr (nursery), brown bullhead (dispersal corridor) and Appalachian crayfish (early summer refuge), and suggest that variation in water level or temperature resulting from climatic change or local anthropogenic activities might modify connectivity between macrohabitats, particularly for younger individuals.

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 score0.046
Threshold uncertainty score0.971

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 head0.024
GPT teacher head0.200
Teacher spread0.176 · 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

Citations9
Published2004
Admission routes1
Has abstractyes

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