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Record W2112664311 · doi:10.1139/f10-151

The effects of handling time, ambient light, and anaesthetic method, on the standardized measurement of fish colouration

2011· article· en· W2112664311 on OpenAlexafffundvenue
Suzanne Gray, Francine L. Hart, Maude E.M. Tremblay, Thomas J. Lisney, Craig W. Hawryshyn

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicFish biology, ecology, and behavior
Canadian institutionsUniversity of GuelphSault CollegeMcGill UniversityQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsFish <Actinopterygii>ReflectivityEugenolLight intensityUltraviolet lightChemistryEnvironmental scienceAnimal scienceBiologyFisheryOptics

Abstract

fetched live from OpenAlex

The effects of handling time, ambient light intensity, and anaesthetic method, on the collection of spectral reflectance data were assessed in two species of Malawi cichlids ( Melanochromis auratus and Metriaclima zebra ). Using spectrometry, colour patches were measured over 10 min, under increasing ambient light levels, and using three anaesthetic methods. As time elapsed, maximum percent reflectance (R max ) decreased across all anaesthetic methods and species, suggesting that measurements should be taken immediately after capture and anaesthetization. With increasing light intensity, R max increased significantly, suggesting that measurements should be taken under natural ambient conditions when possible. Finally, we found that anaesthetizing fish using an ice bath produced significantly higher R max than using either MS-222 (tricaine methanesulphonate) or clove oil (eugenol). However, the highest proportion of ultraviolet (UV) colouration was recorded while fish were anaesthetized with clove oil. Our results highlight the variation involved in measuring fish colour patterns using two related species, and thus indicate the need for a standardized approach to collecting spectral reflectance data in fish.

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)0.002
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 score0.239
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.027
GPT teacher head0.226
Teacher spread0.199 · 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

Citations37
Published2011
Admission routes3
Has abstractyes

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