The Kelp Rescue Initiative: Using science to restore resilient kelp forest ecosystems on B.C.’s coasts.
Bibliographic record
Abstract
Kelp forests cover 36% of the world’s coastlines where they promote biodiversity, provide food and habitat for commercially valuable species, and drive coastal carbon dynamics and nutrient cycling. Across B.C.’s geographically-complex coastlines—including the roughly 10,000 km of linear shoreline in the Salish Sea alone—gradual kelp declines have accelerated in recent years in many areas. Left unchecked, these widespread and accelerating losses along critical salmon migration routes are likely to have far-reaching economic and ecological impacts. Here I describe a new initiative based out of the Bamfield Marine Sciences Centre launched with the purpose of translating scientific knowledge into impactful solutions for scalable kelp forest restoration. The immediate goals of the Kelp Rescue Initiative are to: i) understand fine-scale patterns of population structure and connectivity among kelp forests around Vancouver Island and the Salish Sea, ii) assess the genetic diversity and extent of local adaptation of remnant populations along natural climatic gradients, and iii) act quickly and collaboratively to restore kelp forests with resilient lineages physiologically capable of surviving an increasingly stressful future. These issues affect everyone, and are too big and too urgent to be solved by any lab or group on its own. To scale up these efforts, we must collaborate widely across disciplines. In this talk I will present a roadmap to improve the success of restoration efforts by incorporating diverse tools such as dynamical earth systems modeling, oceanography, species distribution modeling, modern genomic techniques, expansive remote sensing and in-situ observations, community science, and field and laboratory experimentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".