Swimming performance and larval quality are altered by rearing substrate at early life phases in white sturgeon,<i>Acipenser transmontanus</i>(Richardson, 1836)
Bibliographic record
Abstract
To investigate the role of substrate enrichment on larval growth and performance, white sturgeon, Acipenser transmontanus, were reared for 12 dph (the pre-feeding stage) in the presence and absence of enriched substrates (i.e. structurally complex media). Following this period, larval sturgeon were transferred to holding tanks with unenriched substrate (lacking structural complexity) and reared for an additional 30 days, during which time health and performance indicators (growth, Ucrit, startle response reaction time) and whole body lipid composition were assessed at 15 and 18°C. Sturgeon reared on unenriched substrates tended to grow more slowly (up to 40% reduced mass at 40 dph) with a lower condition factor (5–15% lower between 8 and 40 dph), but also exhibited delayed gut development and reduced rate of yolksac absorption (at 15 dph) than those reared with enriched substrates. Whole body lipid composition was significantly altered with substrate enrichment, although the biological relevance of these changes is unknown. White sturgeon reared without exposure to enriched substrates at some temperatures and developmental phases demonstrated modest reductions in aerobic (~20–30% lower Ucrit) and startle response performance (~5–10% slower reaction time) at 15 and 30 dph. Overall, most effects were influenced by rearing temperatures and parentage, such that differences were not statistically significant under all conditions. Clearly, however, substrate enrichment plays an important role in development of white sturgeon during early life stages.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".