Relationships among the species occupancy of marsh birds and vegetation in a wetland ecosystem: a statistics and GIS practicum with the Ottawa National Wildlife Refuge, Lake Erie
Bibliographic record
Abstract
Managing wetland habitats for migrating and native marsh bird species is a major goal of Ottawa National Wildlife Refuge.The purpose of this practicum project was to utilize statistical and GIS skills to improve the knowledge base of how wetland habitat management has been impacting the presence of focal marsh bird species.Using generalized linear mixed models, 14 years of marsh bird data were analyzed using Poisson and logistic regression with 6 wetland sites and 5 vegetation cover types to the 11 focal marsh bird species: Pied-billed Grebe (Podilymbus podiceps), American Coot (Fulica americana), Common Moorhen (Gallinula chloropus), Sora (Porzana carolina), Least Bittern (Ixobrychus exilis), Virginia Rail (Rallus limicola), Black Rail (Laterallus jamaicensis), Yellow Rail (Coturnicops noveboracensis), King Rail (Rallus elegans), and American Bittern (Botaurus lentiginosus) noted by the Marsh Monitoring Program.Piedbilled Grebe abundance was found to be significantly different among years depending on six wetland sites (P<-0.001).Least Bittern occupancy was positively associated with emergent vegetation (P = 0.013).Least Bittern and Sora occupancy was negatively associated with open water (P =0.009, P = 0.013).American Coot occupancy was positively associated with exposed mud/sand/rock cover and tree cover (P = 0.005, P = 0.007).Understanding habitat associations of focal marsh bird species can improve future management plans for the wetland units at Ottawa National Wildlife Refuge.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".