A quantitative analysis of gilthead seabream (<i>Sparus aurata</i>) juvenile dentition as a tool to assess the effect of diet
Bibliographic record
Abstract
Gilthead seabream (Sparus aurata Linnaeus, 1758 (Perciformes, Sparidae)) is an important aquaculture species in the Mediterranean Sea basin. Yet, quantitative data on its dentition under standard farming conditions are currently lacking. Furthermore, it is unknown if the dentition can adapt to food of different sizes. Here, we describe the lower jaw dentition of juvenile S. aurata fed a standard pellet size (4 mm) and present a detailed analysis of 11 representative teeth. Overall, the number of teeth showed large individual variation, but it was not significantly related to fish length. Considerable left–right differences were observed, without clear side dominance. We also assessed the influence of feeding S. aurata a smaller (2 mm) or larger (6 mm) pellet size. Four months of feeding with different pellet sizes did not cause detectable differences in total tooth number on the dentaries at the time of harvest, nor in size of the teeth assumed to be most relevant in food processing. If and how different pellet sizes may nevertheless affect digestion, and eventually fish health, is subject for further studies.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".