InFORMative Science: Monitoring the arrival of Fukushima contamination on the Canadian coast
Bibliographic record
Abstract
The Integrated Fukushima Ocean Radionuclide Monitoring (InFORM) network is a partnership between academic, government, private organizations, and citizen scientists to monitor the arrival of Fukushima-derived radiation, cesium-134 (t1/2 = ~2 years), cesium-137 (t1/2 = ~30 years) in Canadian waters. In response to public demand, monitoring began in the fall of 2014, when models predicted the arrival of radionuclide contamination from the 2011 Fukushima nuclear accident. Monitoring efforts will capture the peak of the radionuclide contamination, predicted in 2016-2017 for our waters, utilizing a network of coastal, oceanic, and biotic sampling. Seawater samples are collected monthly by dedicated citizen scientists in 16 of British Columbia’s coastal communities. Understanding oceanic conditions, through samples collected on research cruises to the NE Pacific (biannual) and the Arctic Ocean (annual), serves as a forecast for the coast. In addition, salmon from each of British Columbia’s major salmon runs are sampled each summer to assess human and ecosystem health risks due to bioaccumulation of Fukushima derived contamination. To date, monitoring has shown levels of radionuclide activity (~10 Bq m-3 in the central NE Pacific) are well below Canadian safe drinking water standards (10,000 Bq m-3). Similarly, radionuclide levels in salmon from 2014 were below the minimum detectable concentration for 134Cs and very low (0.2 Bq kg-1 for 137Cs) compared to safety standards (1,000 Bq kg-1). Through an active, and multi-faceted, outreach campaign these results are providing quality information to the public regarding the accident’s environmental effects here in North America. While contamination levels continue to be below levels that are known to be hazardous to human or ecosystem health, InFORM monitoring is finding levels slightly elevated relative to numerical model predictions. These data will assist in refining models and our understanding of upper-ocean dynamics.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".