A human impact metric for coastal ecosystems with application to seagrass beds in Atlantic Canada
Bibliographic record
Abstract
Coastal biogenic habitats are vulnerable to human impacts from both terrestrial and marine realms. Yet the broad spatial scale used in current approaches of quantifying anthropogenic stressors is not relevant to the finer scales affecting most coastal habitats. We developed a standardized human impact metric that includes five bay-scale and four local-scale (0–1 km) terrestrial and marine-based impacts to quantify the magnitude of anthropogenic impacts to coastal bays and nearshore biogenic habitats. We applied this metric to 180 seagrass beds ( Zostera marina), an important biogenic habitat prioritized for marine protection, in 52 bays across Atlantic Canada. The results show that seagrass beds and coastal bays exist across a wide human impact gradient and provide insight into which are the most and least affected by human threats. Generally, land alteration, nutrient loading, and shellfish aquaculture were higher in the Gulf of St. Lawrence, whereas invasive species and fishing activities were higher along the Atlantic coast. Sixty-four percent of bays were at risk of seagrass decline from nitrogen loading. We also found high within-bay variation in impact intensity, emphasizing the necessity of quantifying impacts at multiple spatial scales. We discuss implications for management and conservation planning, and application to other coastal 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".