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Record W2305356152 · doi:10.1080/15222055.2016.1143419

Mark Retention of Calcein in Cisco and Bloater

2016· article· en· W2305356152 on OpenAlexaboutno aff
Marc A. Chalupnicki, Gregg E. Mackey, Κ. L. Nash, Rich Chiavelli, James H. Johnson, Thomas Kehler, Neil H. Ringler

Bibliographic record

VenueNorth American Journal of Aquaculture · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersU.S. Food and Drug AdministrationU.S. Geological SurveyCisco Systems
KeywordsCalceinOtolithBiologyFish <Actinopterygii>JuvenileFisheryZoologyEcology

Abstract

fetched live from OpenAlex

Abstract Since 2012, a multi-agency initiative to restore these native forage species has been under way. Evaluating the restoration success of Cisco Coregonus artedi and Bloater C. hoyi in Lake Ontario waters requires methods to identify stocked fish. However, juvenile Cisco and Bloater are fragile; thus, mass marking techniques that reduce the handling of individual fish are required and have not previously been evaluated. In 2014–2015 we evaluated the usefulness of calcein (SE-MARK) as a marker on bony structures, including the otolith. Juvenile Bloater and Cisco (14, 100, 128 d old) were immersed in a calcein bath at 5,000 mg/L of water for 4 min to apply the chemical marker. Observations of the marking retention were evaluated 8 d following the treatment. All fish immersed in calcein had strong brilliant marks (rating scale 3) on all bony structures including scales, fin rays, jaw bones, and vertebrate. The otolith was the only hard structure that did not show a brilliant marking due to the opaque nature of the structure. Our results suggest that calcein produces a strong discernable mark on hard bony structures of Cisco and Bloater; however, long-term retention needs further study.

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 score0.018
Threshold uncertainty score0.208

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 science0.0000.000
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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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