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Effect of Feeding Frequency on Growth, Food Conversion Efficiency, and Meal Size of Juvenile Atlantic Sturgeon and Shortnose Sturgeon

2003· article· en· W2067534843 on OpenAlexafffund
A. V. Giberson, Matthew K. Litvak

Bibliographic record

VenueNorth American Journal of Aquaculture · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSturgeonAcipenserBiologyJuvenileFeed conversion ratioFisheryAnimal scienceMealFish <Actinopterygii>ZoologyBody weightEcologyFood scienceEndocrinology

Abstract

fetched live from OpenAlex

We examined the importance of feeding frequency on the growth, conversion efficiency, and meal size of juvenile Atlantic sturgeon Acipenser oxyrinchus and shortnose sturgeon A. brevirostrum. Trials by species were completed consecutively after the fish reached 8 months of age. For both trials, 12 tanks were each stocked with five fish. The feed ration was set at 3% of the tank biomass per day and was adjusted weekly according to increases in tank total biomass. Tanks were randomly chosen to be fed one, four, or eight times during each 24-h period. We found differences in specific growth rate (SGR), corrected food conversion efficiencies (CFCE), and meal size among species. Overall, Atlantic sturgeon grew better, ate more, and exhibited greater feeding efficiencies than shortnose sturgeon, regardless of feeding frequency. Atlantic sturgeon exhibited SGRs of 2.3%/d and conversion efficiencies of 100%. Shortnose sturgeon exhibited growth rates of 0.7–1.6%/d and conversion efficiencies of 42–93%. Only shortnose sturgeon fed four times per day (SGR, 1.6%/d; CFCE, 93%) exhibited results similar to those of Atlantic sturgeon. Atlantic sturgeon had similar growth and feeding efficiencies in all feeding frequency regimes. Shortnose sturgeon fed once per day had significantly lower growth rates (0.7%/d) and feeding efficiencies (42%) than those fed four and eight times per day.

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 score0.045
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.205
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

Citations36
Published2003
Admission routes2
Has abstractyes

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