Data from: Modeling of the larval response of green sea urchins to thermal stratification using a random walk approach
Bibliographic record
Abstract
Larval transport in the ocean can be affected by their vertical position in the water column. In biophysical models that are often used to predict larval horizontal dispersal, generally larval vertical positions are either ignored or incorporated as static parameters. Here, we evaluate the ability of one dimensional random walk based model to predict larval vertical distribution of Strongylocentrotus droebachiensis in response to thermal stratification. Vertical swimming velocities were recorded at various temperatures and used to parameterize the model. Data from a previous laboratory study on the effects of thermal stratification on larval vertical distribution of S. droebachiensis were compared to the model results to evaluate the predictive ability of the model. The model predicts general trends in vertical distribution fairly well, but has a systematic bias which can be explained by un-quantified larval behaviors at the boundaries of the experimental water column. Overall, our behavioral model successfully reproduces the mechanism which regulates larval vertical distribution in response to thermal structure. Collectively, the findings suggest that simple behavioral models parameterized using simple lab experiments can prove useful in estimating the vertical distributions of invertebrate larvae in the laboratory and likely in the ocean. Such models can then be linked to bio-physical models to more accurately predict larval dispersal.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".