Incorporating anthropogenic thresholds to improve understanding of cumulative effects on seagrass beds
Bibliographic record
Abstract
Cumulative human impact analysis is a promising management tool to estimate the impacts of stressors on ecosystems caused by multiple human activities. However, connecting cumulative impact scores to actual ecosystem change at appropriate spatial scales remains challenging. Here, we calculated cumulative effects (CE) scores for 187 seagrass beds in Atlantic Canada that accounts for both bay-scale and local-scale anthropogenic activities. We then developed a CE threshold to evaluate where degradation of seagrass beds from multiple human activities is more likely. Overall, the CE score was the best predictor of human impacts for seagrass beds. Locations with high watershed land alteration and nitrogen loading had the highest CE scores; however, we also identified seagrass beds with high CE scores in regions characterized by generally low levels of human activities. Forty-nine seagrass beds exceeded the CE threshold and, of these, 86% had CE scores that were influenced by three or more stressors that cumulatively amounted to a large score. This CE threshold approach can provide a simplified metric to identify areas where management of cumulative effects should be prioritized and further highlights the need to consider multiple human activities when assessing anthropogenic impacts to coastal habitats.
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 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) | 0.001 | 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 teacher head, 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".