Baseline expression of heat-shock proteins (HSPs) of a "thermotolerant" Mediterranean marine species largely influenced by natural temperature fluctuations
Bibliographic record
Abstract
Northwestern Mediterranean warming-related disease outbreaks and species shifts have recently been documented. Biomarkers of short-term effects on the health or resistance of organisms are necessary to assess and understand mechanisms affecting marine biodiversity. Heat-shock protein (HSP) expression was here believed to present finer variations than previously reported in experimental studies, which we tested to assess HSP sensitivity and relevance in the global warming context. One population of the "thermophilic" marine cave mysid Hemimysis margalefi was monitored over 4 years. Three HSPs presented recurrent expression. Among them, HSP50 and HSP60 expressions varied seasonally with environmental temperature. HSP60 expression displayed the greatest variability and was most closely related to temperature fluctuations. Multiple regression showed that while the main stressor was temperature, demographic variables (proportion of immatures, proportion of gravid females) also influenced expression variability. Such a monitoring of HSP baseline expression in a natural population had only rarely been conducted. HSPs are, however, good biomarkers to evaluate the effects of warming episodes or heat stress in the wild. This study provides essential spatial and temporal reference for further experimental and in situ analyses and is a prerequisite to diagnostic and predictive approaches.
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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) | 0.000 | 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".