Spatial and temporal persistence of nearshore kelp beds on the west coast of British Columbia, Canada using satellite remote sensing
Bibliographic record
Abstract
Abstract Bull kelp Nereocystis luetkeana is an important foundation species, providing structural habitat and nutrients to the nearshore ecosystems of temperate coastal regions in the Northeast Pacific. Sensitive to environmental conditions, this species thrives in cool, nutrient‐rich water. Reported declines in the extent and distribution of bull kelp may reflect changing oceanic conditions and result in breakdown of important food chains and ecosystem services. This study uses satellite remote sensing to map kelp bed extent from 2004 to 2017 in the Salish Sea on the West Coast of British Columbia, Canada and examines the relationship between trends in kelp persistence with local and global scale environmental conditions. In our study area, we found limited evidence of kelp decline. Local scale effects of current speed, temperature and substrate type may play a role in the spatial and temporal patterns of persistence. Kelp persistence was higher in sites with rocky substrate and lower in areas with low current and gravel or sand substrate. A decline in kelp was recorded from a high in 2015 to a low in 2017; however, a longer and more complete record is needed to distinguish declining trends from natural variability. This work highlights the importance of continued collection of long‐term data for use in time series of kelp abundance as multiple factors can influence the reliability of image interpretation and kelp classification.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".