MétaCan
Menu
Back to cohort

Association between biogeographical factors and boreal lake fish assemblages

2005· article· en· W2130013355 on OpenAlexaboutno aff
Michael C. van Zyll De Jong, I. G. Cowx, D. A. Scruton

Bibliographic record

VenueFisheries Management and Ecology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEcologyFish <Actinopterygii>BorealCanonical correspondence analysisScale (ratio)Environmental scienceFisherySpecies richnessBiologyCartography

Abstract

fetched live from OpenAlex

Abstract Five regions in insular Newfoundland Canada, comprising 152 lakes, were studied to identify associations between species composition (presence and absence), geographical location and environmental variables (pH, area, depth, alkalinity, secchi disc depth and shoreline development factor). Correspondence analysis and canonical variate analysis were used to distinguish regional patterns. Five biologically and environmentally distinct areas were identified. The degree of association between biological, environmental and geographical distances were contrasted using Mantel's test. Regional fish community structure was significantly correlated with large‐scale geographical distance but not with environmental parameters or small scale distance. It was proposed that large scale processes such as post‐glacial dispersion, climate and recent species introductions are important determinates in structuring regional fish assemblages. Differences in individual lake character were important determinates in intraregional variability in fish assemblage type. Sampling strategies for regional modelling and management are discussed.

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 score0.089
Threshold uncertainty score1.000

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 science0.0000.001
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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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

Citations13
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueFisheries Management and EcologySame topicFish Ecology and Management StudiesFrench-language works237,207