Laboratory‐based measures of temperature preference and metabolic thermal sensitivity provide insight into the habitat utilisation of juvenile <scp>California horn shark (</scp><i>Heterodontus francisci</i><scp>)</scp> and <scp>leopard shark (</scp><i>Triakis semifasciata</i><scp>)</scp>
Bibliographic record
Abstract
Abstract Laboratory‐based studies examining fish physiological and behavioural responses to temperature can provide important insight into species‐specific habitat preferences and utilisation, and are especially useful in examining vulnerable life stages that are difficult to study in the wild. This study couples shuttle box behavioural experiments with respirometry trials to determine the temperature preferences and metabolic thermal sensitivity of juvenile California horn shark (Heterodontus francisci) and leopard shark (Triakis semifasciata). As juveniles, these two species often occupy similar estuarine habitats but display contrasting behaviours and activity levels – H. francisci are relatively sedentary, whereas T. semifasciata are more active and mobile. This study shows that juvenile H. francisci and T. semifasciata have comparable thermal preferences and occupy similar temperature ranges, but H. francisci metabolism is more sensitive to acute changes in temperature as expressed through a higher Q10 (H. francisci = 2.58; T. semifasciata = 1.97; temperature range: 12–24°C). Underlying chronic temperature acclimation to both warm (21°C) and cool (15°C) representative seasonal temperatures did not appear to significantly affect these parameters. These results are discussed in the context of field studies examining known distributions, habitat and movement patterns of H. francisci and T. semifasciata to better understand the role of temperature in species‐specific behaviour. Juvenile H. francisci likely target thermally stable environments, such as estuaries that are close to their preferred temperature, whereas juvenile T. semifasciata metabolism and behaviour appear less dependent on temperature.
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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) | 0.000 | 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) | 0.002 | 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".