Inorganic nitrogen has a dominant impact on estuarine eelgrass distribution in the Southern Gulf of St. Lawrence, Canada
Bibliographic record
Abstract
Abstract Eelgrass (Zostera marina) coverage and height were evaluated in 16 estuaries in the Southern Gulf of St. Lawrence, Canada, using boat‐based sonar surveys to determine the dominant factors in the decline of eelgrass in the region. Eelgrass coverage was modeled from the sonar surveys and quantified as the percentage of available habitat occupied—with habitat being defined by salinity limits (> 10 PSU) and depth (< 3 m). Estuaries showed a marked variation in eelgrass coverage ranging between 6% and 57% of available habitat, with eelgrass absent in the upper estuary of estuaries with the highest nitrate loading. The Dunk River estuary showed a decline in eelgrass coverage between 1967 and 2014, a period of increasing nitrogen loading. Measures of eelgrass height were not related to coverage endpoints, suggesting that height variables are not suitable endpoints for overall eelgrass health. Analysis of the influence of environmental factors showed that the factor that consistently correlated to eelgrass coverage was nitrate‐N loading while the factor most influencing eelgrass height was light attenuation. A nonlinear logistic loading‐effect model relating eelgrass coverage to nitrate‐N loading indicated that 10%, 25%, 50%, 75%, and 90% decline in eelgrass would be expected to occur in response to estuarine nitrate‐N loads of 1.3 kg ha−1 yr−1, 8.0 kg ha−1 yr−1, 50.0 kg ha−1 yr−1, 312 kg ha−1 yr−1, and 1947 kg ha−1 yr−1, respectively. These results suggest that inorganic nitrogen loading is the most significant factor to be addressed by environmental managers when addressing eelgrass declines in estuaries similar to those studied.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".