Video Microscopy Detection of Oyster Spat Heart Rate (HR): Acclimation temperature alters HR response to acute temperature change
Bibliographic record
Abstract
We developed a non‐invasive method to measure oyster spat heart rate (HR) during a cooling/warming protocol. Triploid spat (1.1–1.8mm; 3–4 months) were acclimated to 10°C (“cool”; n=5) or 22°C (“hatchery”; n=5), and exposed to a temperature ramp protocol (22°C to 10°C to 22°C; 2°C intervals, 10 minutes per set‐point). Hearts were visualized using video acquisition/image analysis, and HR identified by commercially‐available software. HR was directly related to temperature in both groups. HR at each set‐point did not vary between cooling/warming; however, “hatchery” spat displayed consistently elevated HR vs. “cool” spat. Heart rate variability (HRV) was inversely related to temperature in both groups. “Cool” spat consistently displayed elevated HRV vs. “hatchery” spat, and displayed greater HRV during cooling vs. warming. Spat also displayed periods of asystole of varying lengths, which were more prevalent and prolonged at low temperatures, (“hatchery”: 5/5 asystolic at set‐point 10°C, vs. “cool”: 1/5 asystolic at set‐point 10°C). Our method provides a robust means of assessing HR in a simple, invertebrate model. Our data suggest that the HR phenotype of commercial oyster spat is altered by different acclimation temperatures, which may be useful in understanding spat thermal adaptation and growth in ocean aquaculture environments.
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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 | 0.001 | 0.001 |
| 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".