Assessment of 3D Spatial Interpolation Methods for Study of the Marine Pelagic Environment
Bibliographic record
Abstract
Given the volumetric nature of the ocean, 3D spatial modeling and interpolation could be a key to a better understanding of continuous abiotic and biotic phenomena that compose the marine ecosystem, although such techniques are rarely used and their actual performance is poorly studied. Here, we evaluate the performance of 3D spatial interpolation for five pelagic variables derived from a typical oceanographic campaign conducted in the southeastern Beaufort Sea (Canadian Arctic) in 2009. Our main objective is to evaluate and compare the performance of a deterministic interpolation method (inverse distance, IDW) and a geostatistical method (ordinary kriging, OK) with a variation of method input parameters (search neighborhood, weights) for variables with increasing complexity in terms of data anisotropy and sampling configuration. Performance of different 3D interpolation strategies is evaluated by cross-validation and a qualitative comparison of 3D spatial models. Our results show that OK was the optimal method. However, when the complexity of pelagic variables increased in terms of spatial autocorrelation and data variation, the error difference between OK and IDW was reduced. We recommend that recent advances in spatial 3D modeling tools developed primarily for geological modeling should be exploited to extend the usual interpretation of marine pelagic phenomena from a 2D to a 3D 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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".