Transformative Sea-level Rise Research and Planning: Establishing a University, Tribal, and Community Partnership for a Resilient California North Coast
Bibliographic record
Abstract
Sea-level rise (SLR) is and will continue to be a pressing issue in the rural, North Coast region of California, especially since nearby Wigi (or Humboldt Bay) is experiencing one of the fastest rates of relative SLR on the U.S. West Coast. In this paper, we argue that SLR presents a transformative opportunity to rekindle environmental relationships and reshape the future of the California North Coast and beyond. As the preeminent higher education institution of the region, Cal Poly Humboldt has the responsibility to be a leader in education, research, and planning for climate resilience. We describe efforts of the Cal Poly Humboldt Sea Level Rise Institute to establish a university-Tribal-community partnership that braids together different approaches and ways of knowing to develop research and planning that supports a resilient California North Coast. Since Wigi is projected to experience the effects of SLR sooner than the rest of the state, the North Coast region is poised to act as an incubator for new ideas and solutions, including Indigenous knowledge systems, and to play a role in influencing equitable, resilient, and transformative SLR adaptation processes in other parts of the state and the world. This will require developing programming and expertise in specific disciplinary areas, but, more importantly, will require the development of opportunities and spaces for various disciplines, ways of knowing, and sectors (e.g. Tribal nations, academia, government, NGOs, private companies, and community groups) to converge and bring the best of what they have to address climate-induced challenges and opportunities.
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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 | 0.016 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".