Accounting for Spatial Autocorrelation in Great Lake Coastal Wetland Ecological Responses
Bibliographic record
Abstract
The Great Lakes Coastal Wetland Monitoring Program (CWMP) collects wetland biota, habitat, and water quality in order to provide information on the health of the Great Lakes. My research used CWMP data on fish, birds, amphibians, wetland vegetation, aquatic macroinvertebrates, and water quality from 185 wetlands across the Great Lakes collected during the peak growing season of 2016, 2017, and 2018. My research goal was to determine if wetland vegetation cover helped shape the water quality of the wetland. However, in order to investigate these connections, I needed to first overcome the spatial influence in the data. The project data was manipulated using Microsoft Excel and R in order remove data gaps and inconsistencies, then ArcGIS was used to determine the best way to account for spatial autocorrelation in the data. Spatial autocorrelation can be used along with spatial data to make accurate generalizations about relatedness over a greater area, the idea being that close points are more similar than further ones. This is generally a good geospatial tool to use when gaps are present in the data. For the CWMP data, however, it presents a challenge because of the diverse nature of the Great Lake ecosystem and strong spatial gradient of ecosystem quality from Lake Superior (North) to Lake Ontario (Southeast). As such, to investigate connections between wetland, water quality, and vegetation across wetlands, the within lake spatial patterns must be accounted for. In order to accurately gauge the health of the wetlands across the study area, my research attempts to account for spatial autocorrelation in these data. The final products of this research will be maps of the study wetlands and the Great Lakes that display how location influences ecological relationships across the Great Lakes coastal wetlands.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".