Regional classification using gradients of marine species assemblages: a data-driven approach to modelling marine ecosystems
Bibliographic record
Abstract
Marine management and conservation efforts often rely on predictive modelling of species observations, the output of such models can be influenced by their regional extent.This study proposes a data-driven classification of marine regions by clustering modelled gradients of species assemblages.Two clustering methods are considered, the CLARA algorithm and mean-shift segmentation, and compared with depth and geographically stratified regions.Regional classification was applied to models using three methods: Regional indices as categorical predictors, regional ensemble models, and a pre-calibration regional data-filter.Regional influence was measured in changes of MSE and R 2 values.Large changes in model output were restricted to a small number of anomalous species models.Mean-shift clustered regions produced moderately improved MSE and R 2 values compared to the other methods.Regional influence in the species distribution models were shown to be species dependent, necessitating an assessment of relevant species included in regional classification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".