A human impact metric for coastal ecosystems with application to seagrass beds in Atlantic Canada
Bibliographic record
Abstract
Abstract Coastal biogenic habitats are particularly vulnerable to cumulative human impacts from both terrestrial and marine realms. Yet the broad spatial scale used in current global or regional approaches of quantifying multiple anthropogenic stressors are not relevant to the local or bay-wide scales affecting most coastal biogenic habitats. To fill this gap, we developed a standardized human impact metric to quantify the magnitude of anthropogenic impacts to coastal ecosystems more broadly, and biogenic habitats in particular. We applied this metric to 180 seagrass beds ( Zostera marina ), an important biogenic habitat prioritized for marine protection, across Atlantic Canada. Our impact metric includes five bay-scale and four local-scale terrestrial and marine-based impacts. Results show that seagrass beds and coastal bays in Atlantic Canada exist across a wide gradient of human impacts. Considerable differences in the range and intensity of impacts within and between regions provide insight into where coastal bays and seagrass ecosystems are expected to be most and least affected by individual or cumulative human threats. We discuss implications for management and conservation planning, and the general application of our impact metric to other coastal regions and habitats in Canada and beyond.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| 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) | 0.002 | 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".