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Record W2888247709 · doi:10.1139/cjfas-2018-0026

Avoidance of carbon dioxide in flowing water by bighead carp

2018· article· en· W2888247709 on OpenAlexvenueno aff
Caleb T. Hasler, Christa M. Woodley, Eric V. C. Schneider, Bryton K. Hixson, Cynthia J. Fowler, Stephen R. Midway, Cory D. Suski, David L. Smith

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersU.S. Geological SurveyU.S. Environmental Protection Agency
KeywordsBighead carpCarbon dioxideHypophthalmichthysFlumeFisheryEnvironmental scienceFish <Actinopterygii>Animal scienceBiologyCarpEcologySilver carpPhysicsMechanics

Abstract

fetched live from OpenAlex

Carbon dioxide (CO2) in water has been explored for use as an invasive species deterrent system. To date, studies have not determined CO2 avoidance by fish in flowing water, and this is a necessary step before an operational deterrent system can be implemented. The objective of the study was to define how flowing water influences the response of bighead carp (Hypophthalmichthys nobilis) to continuous plugs of CO2. A choice experiment by which CO2 was injected into channels of an annular flowing water flume was completed. In trials when CO2 was injected into the inner vein, fish spent less time in the vein when compared with control conditions. An increased amount of lateral movements and reduced performance were also observed when fish were exposed to elevated CO2. The study demonstrates that bighead carp in flowing water enriched with CO2 move away, a finding consistent with static water experiments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.196
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 source (direct Gemma or distilled Codex), 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

Citations16
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicFish Ecology and Management Studies→French-language works237,207→