Biological-physical coupling in a highly advective ecosystem: Through a lens of diel vertical migration
Bibliographic record
Abstract
A mismatch in sampling resolution between biology and physics has been one of the fundamental challenges in advancing our understanding of ecological processes in the ocean. The recent deployment of cabled observatories enables the collection of active acoustic data with coverage and resolution comparable to physical data, providing an unprecedented ability to observe the behavior of zooplankton that are critical trophic link in the food web. Using multifrequency acoustics, acoustic Doppler current profiler (ADCP), and meteorological sensors deployed by the Ocean Observatories Initiatives (OOI) in the Northern California Current System, we explored small-scale upwelling impacts on diel vertical migration exploiting the frequency and predictability of this ubiquitous behavior. We found that the vertical extent of diel vertical migration changed relative to upwelling intensity. Migration behavior was completely suppressed during strong downwelling periods. Zooplankton migrated throughout the water column during relaxation periods while decreasing their migration distance as the winds shifted to upwelling favorable conditions, potentially increasing their accessibility to food resources. These results suggest that animals adapt their behavior to accommodate changes in the physical environment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".