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Record W2096232098 · doi:10.1080/15222055.2014.893472

A Self-Contained, Controlled Hatchery System for Rearing Lake Whitefish Embryos for Experimental Aquaculture

2014· article· en· W2096232098 on OpenAlexafffund
Charles Mitz, Christopher Thome, Mary Ellen Cybulski, Lisa Laframboise, Christopher M. Somers, Richard G. Manzon, Joanna Y. Wilson, Douglas R. Boreham

Bibliographic record

VenueNorth American Journal of Aquaculture · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsNOSM UniversityUniversity of ReginaMcMaster University
FundersBruce PowerMinistry of Natural Resources
KeywordsHatcheryBiologyAquacultureFisheryEmbryoZoologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract A self-contained, small-scale research hatchery was constructed in a modified chromatography refrigerator equipped with a filtered and UV-sterilized water recirculation system. Lake Whitefish Coregonus clupeaformis embryos were raised in conventional upwelling hatching jars, in dishes with a continuous slow “drip feed,” and in a variety of static water incubation systems in petri dishes and multiwell plates. The optimal rearing density for petri dishes was found to be 50 embryos per dish, with weekly water changes. The highest survival in multiwell plates was seen in the 6- and 24-well sizes. Survival rates in most multiwell plates and petri dishes, as well as in the hatching jar incubators, were between 40% and 60%, which is in line with survival rates seen in commercial large-scale rearing. Overall, these techniques permitted the rearing of large numbers of whitefish in separate batches and under controlled conditions, while greatly reducing space requirements and material costs. Our system is well suited for research and other situations requiring the temperature-controlled rearing of embryos on a small scale.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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 designBench or experimental
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

Citations26
Published2014
Admission routes2
Has abstractyes

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