Effect of Feeding Frequency on Growth, Food Conversion Efficiency, and Meal Size of Juvenile Atlantic Sturgeon and Shortnose Sturgeon
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".