Use of GIS and remotely sensed data for<i>a priori</i>identification of reference areas for Great Lakes coastal ecosystems
Bibliographic record
Abstract
Identification of reference conditions for ecological assessments of coastal ecosystems poses a challenging problem in highly modified landscapes. A method is described for characterizing disturbance in coastal ecosystems using remotely sensed land classification and other publicly available GIS data. Within ecoregions bordering the US Great Lakes coast, aquatic habitats bordering the shoreline were classified into five ecological types: high‐energy shoreline, embayments, open‐coast, river‐influenced and protected wetlands. Degree of anthropogenic disturbance in contributing areas to these ecosystems was assessed using a watershed approach for wetland types or a moving window approach for high‐energy shorelines. Anthropogenic stress variables included proportions of agricultural or residential land use, information on population and road density, and distance from the nearest point source. Polygons (wetlands) or pixels (high‐energy shoreline) were categorized as ‘reference’ if the magnitude of the most severe stressor, based on its cumulative frequency distribution within that ecoregion, placed it within the lowest 20th percentile. For shorelines, adjacent ‘reference’ pixels were agglomerated into polygons and a final ranking of polygons containing at least 2 km of shoreline was used to identify candidate reference areas. A subset of these sites is currently being sampled for fish, macroinvertebrates and physical habitat attributes. This a priori approach to reference area identification will allow managers to identify biological correlates of reference conditions, providing a benchmark for bioassessment and restoration efforts in coastal regions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".