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Record W4389545838 · doi:10.1002/fsh.11028

Fish Diversity and Use of Nearshore and Open-Water Habitats in Terminal Lakes

2023· article· en· W4389545838 on OpenAlexaff
Zachary Bess, Aaron A. Koning, James Simmons, Erin Suenaga, Aldo San Pedro, Joshua Culpepper, Facundo Scordo, Carina Seitz, Suzanne J. Rhoades, Tara McKinnon, Ryan McKim, Karly Feher, Flavia Tromboni, Julie W. Regan, Sudeep Chandra

Bibliographic record

VenueFisheries · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsYork University
Fundersnot available
KeywordsFish <Actinopterygii>FisheryHabitatOpen waterTerminal (telecommunication)Diversity (politics)Environmental scienceEcologyBiologyEngineering

Abstract

fetched live from OpenAlex

Abstract Terminal lakes face conservation challenges due to consumptive water use and changes in climate. We quantified the extent of the littoral and open water zones in 18 terminal lakes spanning five continents and show that lake level declines produce variable changes in littoral zone surface area. While littoral zones account for a small portion of the habitat in these lakes, 77% of the fish species inhabit these zones and 87.5% consume littoral–benthic organisms. We found that littoral zone surface area correlates with littoral zone fish species richness (P &amp;lt; 0.01; R2 = 0.47) as well as the number of species relying on benthos (P &amp;lt; 0.01; R2 = 0.44). However, we found (1) no correlation between the percent of the lake's surface area that is littoral and the percent of the fish community that inhabits the littoral zone (Pearson's r = 0.3; P = 0.3), and (2) no correlation between the percent of the lake's surface area that is littoral and the percent of the fish community that consumes benthic organisms (Pearson's r = −0.1; P = 0.8). Because many terminal lakes are desiccating, conservation of biodiversity in the nearshore zones of these lakes may be warranted.

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 score0.031
Threshold uncertainty score0.857

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 science0.0000.003
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 head0.035
GPT teacher head0.222
Teacher spread0.187 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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