The effects of short space and time scale current variability on the predictability of passive ichthyoplankton distributions: an analysis based on HF radar observations
Bibliographic record
Abstract
Abstract The importance of small scale variations in currents on the predictability of spatial distributions of fish eggs is investigated using dense observations of surface currents. The currents were measured with HF radar and used to drive an advection–diffusion model of ichthyoplankton concentration. We first demonstrate that the model produces acceptable agreement with observed egg fields. We then use the predicted egg fields as a basis for comparison with model runs made with currents subsampled in space and filtered in time. Significant error was found for spatial sampling intervals as small as 3 km, even though most of the variance in the currents occurred at much longer length scales. This was primarily due to the loss of the rich small scale variability in horizontal divergence. Nevertheless, most of the error is due to the misfit in the egg fields at larger length scales; that is, small scale forcing is necessary in this system for the larger scale features to be reproduced. This study thus suggests that considerable caution should be exercised before assuming that circulation models, even very sophisticated and detailed ones, capture enough of the variability of marine systems to make accurate forecasts of plankton distributions.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".