Pathways, impacts and fate of marine debris generated by the 2011 tsunami in Japan, derived from a synthesis of numerical models and observational reports
Bibliographic record
Abstract
March 11, 2011 tsunami devastated the east coast of Japan and produced millions of tons of marine debris that drifted across the North Pacific. The extraordinary amount and unusual composition of tsunami debris allowed to trace its drift across the ocean and arrivals on remote shores and helped to better understand the pathways of floating marine debris. A suite of ocean models synthesized with these observations produced most complete picture of the debris dynamics, pathways, and fate. While observations allowed to optimize such model parameters as windage and source distribution, the models filled large gaps in sparse observations and produced estimates of the total budgets. For example, the study suggests that the original number of boats lost to the tsunami was about 1,000 and about 100 of these boats may be still floating in the ocean. Detailed analysis of model fluxes on the US/Canada West Coast and their comparison with reports of tsunami boats from the same region revealed serious difficulties that even best OGCMs (Ocean General Circulation Models), such as the HYCOM (Hybrid Coordinate Model, operated by the US Navy and used for coordination of such operational activity as oil spill response), have serious problems with reproducing even main peaks in observations. The best correspondence was achieved in a simple diagnostic model (SCUD), whose coefficients were optimized using historical data of satellite altimetry, scatterometry and trajectories of Lagrangian floats (Figure). In this presentation we discuss methods available to numerically study the drift of marine debris (particles versus tracer), to calibrate/validate models using sparse observational data and to maximize the utility of satellite and model products in various applications. Figure. Model fluxes on the North America west coast between 40 and 51N as function of time and windage. Rows (from top to bottom) correspond to SCUD (a and f), SCUD-HYCOM (b and g), MOVE (c and h), FORA (d and i), and GNOME (e and j) models. Left column shows original model fluxes and right column same fluxes smoothed in time with a Gaussian 1.5-month half-width filter. White dots and lines mark peaks in model fluxes for different windages. Vertical magenta lines mark five main peaks in observations. Horizontal grey lines mark the optimal windage parameter values, derived from model comparison with boat reports, and span over the periods of the comparison. Color scale is strongly nonlinear and model flux units are fraction of the released tracer per a year. White dashed line in (d) illustrates faster drift and earlier arrival of higher windages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".