Multiscale characterization of spatial relationships among oxycline depth, macrozooplankton, and forage fish off Peru using geostatistics, principal coordinates of neighbour matrices (PCNMs), and wavelets
Bibliographic record
Abstract
Upwelling ecosystems are particularly heterogeneous and present intense mesoscale (tens of kilometres) and submesoscale (hundreds of metres to kilometres) activity that are expected to drive the distribution of the organisms and thus their interactions. Here we addressed the impact of the physical forcing in the northern Humboldt Current system off Peru, which is characterized by the presence of an intense and shallow oxygen minimum zone and used the variability of the depth of the oxycline as a proxy of the physical forcing that impacts the epipelagic communities. We analyzed simultaneous high-resolution acoustic observations of the oxycline depth, the biomass in macrozooplankton, and the biomass in pelagic fish. Three complementary methodologies were considered: (i) geostatistical methods and correlation tests, (ii) principal coordinates of neighbour matrices, and (iii) wavelet analysis. Our results highlight the relevance of a multimethod framework to characterize the multiscale relationships between marine ecosystem components. We also provided evidence that the submesoscale-to-mesoscale variability of the oxycline depth drives the distribution of macrozooplankton, which further structures the distribution of forage fish in a bottom-up cascade.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".