Interactions between discrete events and continuous dynamics in the regulation of scallops valve opening: insights from a biophysical model
Bibliographic record
Abstract
Abstract This study constitutes a first attempt to quantify processes that govern valve gape dynamics in bivalves. We elected to focus on the scallop, Pecten maximus , not only because of its economic importance but also because it has a complex behaviour and high sensitivity to stress, which can be inferred from valve gape dynamics. The adductor muscle is the primary organ implicated in valve movements. Scallops, as other bivalves, move their valves sharply to ensure basic physiological functions or to respond to stressing conditions; these sharp events can be perceived as discrete events within a continuous dynamic. A biophysical model, originally designed for human muscles, was first selected to simulate the adductor muscle contraction, countering the passive valve opening by the umbo ligament. However, to maintain the possibility of rapid valve movements, described as typical of bivalves behaviour, it was necessary to modify the model and propose an original formulation. The resulting hybrid modelling simulates how valve opening tends to converge continuously toward a stable steady-state angle, while being interspersed with discrete, sharp closing events, deviating values from this equilibrium. The parameters of the new model were estimated by optimization using Hall-Effect Sensor valvometry data recorded in controlled conditions. Equilibrium of the continuous regime (when fiber activation equals deactivation) was estimated for a gape angle close to ca . 15 degrees, which is ca . 45% of the maximum opening angle, hence implying a constant effort produced by the adductor muscle. The distribution of time intervals between two successive discrete events did not differ significantly from a random process, but the peak amplitudes deviated from randomness, suggesting they are regulated physiologically. These results suggest that discrete events interact with continuous dynamic regimes, regulating valve opening to minimize physiological efforts and conserve energy. However, because the overall physiological state of the scallop organism conditions the activity of the adductor muscle, a complete understanding of the physiology of bivalves will require linking a more comprehensive model of valve gape dynamics with experimental observations of physiological energy consumption under different conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".