Sea otter recovery buffers century-scale declines in California kelp forests
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The status of kelp forests and their vulnerability to climate change are of global significance. As the foundation for productive and extensive ecosystems, understanding long-term kelp forest trends is critical to coastal ecosystem management, climate resiliency, and restoration programs. In this study, we curate historical US government kelp canopy inventories, develop methods to compare them with contemporary surveys, and use a machine learning framework to evaluate and rank the drivers of change for California kelp forests over the last century. Historical surveys documented Macrocystis and Nereocystis kelp forests covered approximately 120.4 km 2 in 1910–1912, which is only slightly above surveys in 2014–2016 (112.0 km 2 ). These statewide comparisons, however, mask dramatic regional changes with increases in Central California (+57.6%, +19.7 km 2 ) and losses along the Northern (-63.0%, -8.1 km 2 ), and Southern (-52.1%, -18.3 km 2 ) mainland coastlines. Random Forest models rank sea otter ( Enhydra lutris nereis ) population density as the primary driver of kelp changes, with benthic substrate, extreme heat, and high annual variation in primary productivity also significant. This century-scale perspective identifies dramatically different outcomes for California’s kelp forests, providing a blueprint for nature-based solutions that enhance coastal resilience to climate change.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 it