Strategies for simulating the drift of marine debris
Bibliographic record
Abstract
Modelling the drift of marine debris in quasi-real time can be of societal relevance. One pertinent example is Malaysia Airlines flight MH370. The aircraft is assumed to have crashed in the Indian Ocean, leaving floating wreckage to drift on the surface. Some of these items were recovered around the western Indian Ocean. We use ocean currents simulated by an operational ocean model in conjunction with surface Stokes drift to determine the possible paths taken by the debris. We consider: (1) How important is the influence of surface waves on the drift? (2) What are the relative benefits of forward- and backward-tracking in time? (3) Does including information from more items refine the most probable crash-site region? Our results highlight a critical contribution of Stokes drift and emphasise the need to know precisely the buoyancy characteristics of the items. The differences between the tracking approaches provide a measure of uncertainty which can be minimised by simulating a sufficiently large number of virtual debris. Given the uncertainties associated with the timings of the debris sightings, we show that at least 5 items are required to achieve an optimal most probable crash-site region. The results have implications for other drift simulation applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".