Peer Review #1 of "Exploring spatial nonstationary environmental effects on Yellow Perch distribution in Lake Erie (v0.2)"
Bibliographic record
Abstract
Background: Global regression models under an implicit assumption of spatial stationarity were commonly applied to estimate the environmental effects on aquatic species distribution.However, the relationships between species distribution and environmental variables may change among spatial locations, especially at large spatial scales with complicated habitat.Local regression models are appropriate supplementary tools to explore species-environment relationships at finer scales. Method:We applied geographically weighted regression (GWR) models on Yellow Perch in Lake Erie to estimate spatially-varying environmental effects on the presence probabilities of this species.Outputs from GWR were compared with those from generalized additive models (GAMs) in exploring the Yellow Perch distribution.Local regression coefficients from the GWR were mapped to visualize spatially-varying species-environment relationships.K-means cluster analyses based on the t-values of GWR local regression coefficients were used to characterize the distinct zones of ecological relationships.Results: GWR resulted in a significant improvement over the GAM in goodness-of-fit and accuracy of model prediction.Results from the GWR revealed the magnitude and direction of environmental effects on Yellow Perch distribution changed among spatial locations.Consistent species-environment relationships were found in the west and east basins for adults.The different kinds of speciesenvironment relationships found in the central management unit implied the variation of relationships at a scale finer than the management unit. Conclusions:This study draws attention to the importance of accounting for spatial nonstationarity in exploring species-environment relationships.The GWR results can provide support for identification of unique stocks and potential refinement of the current jurisdictional management unit (MU) structure toward more ecologically relevant MUs for the sustainable management of Yellow Perch in Lake Erie.
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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 | 0.013 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.343 | 0.155 |
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".