Incorporating spatial and temporal marine species distribution and vulnerability into tribal oil spill response decision-making: a project of the Makah Tribe oil spill response working group
Bibliographic record
Abstract
Since the 1970s, over 2 million gallons of oil have been spilled in the marine and coastal Treaty Areas of the Makah Tribe. Since time immemorial the Makah people, culture, subsistence, and economy have depended upon the ocean and its bounty. Vessel traffic on both sides of the U.S.-Canadian border is expected to continue to increase, especially in areas which overlap with the Makah Treaty Area. It’s not a question of if, but when another oil spill will occur, putting culturally and economically important resources at risk. In addition to the impacts of a spill itself, response methods also pose significant risks to human and ecological health. If a spill occurs, the Makah Tribe will hold a decision-making role in the Unified Command System. To build and coordinate internal tribal capacity, support robust decision-making that protects sensitive resources, and respond effectively to a spill, the Makah Tribe created an Oil Spill Working Group (OSWG). One project of the OSWG is to assess the trade-offs of oil spill response methods and develop a decision-making framework to minimize environmental impacts. To capture the spatial and temporal distribution of culturally and economically important marine resources, the OSWG decided to incorporate this information directly into the decision-making framework, along with ocean conditions which determine response method viability. After a literature review of ecological impacts of response methods, we created a seasonal calendar and set of maps which outline the distribution of sensitive marine resources to inform tribal decision-making around spill response methods. This tool is unique in that it was created through a collaborative and cross-departmental working group, incorporates spatial and temporal aspects of resources vulnerability to spills, and is aimed at building tribal capacity to actively engage in spill response decision-making to protect Treaty Resources.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".