Developing the first binational dreissenid mussel biomass map for Lake Erie
Bibliographic record
Abstract
Dreissena rostriformis bugensis and Dreissena polymorpha , collectively known as dreissenid mussels, were first documented in Lake Erie nearly 40 years ago and continue to strongly impact ecosystem structure and function. Recently, dreissenids have been recognized as the primary regulator of phosphorus dynamics in the Great Lakes, potentially reducing external phosphorus load management strategies. To evaluate the extent that dreissenids may modulate phosphorus dynamics and assess the effectiveness of external load management strategies, ecosystem models need to adequately represent spatial patterns of dreissenid biomass densities. Assessment of dreissenid populations in the Great Lakes are complicated by their vast size, as well as the heterogeneity and stochastic nature of critical physical variables that shape habitat suitability (e.g., depth, hypoxia, substrate type, and benthic shear stress). We used observations from a 2019 benthic survey and 10-fold cross validation to assess spatial prediction methods to create a spatially explicit data layer representing dreissenid biomass in Lake Erie, including inverse distance weighting, nearest neighbour, depth binned average, random forest, generalized linear, and general additive models. A random forest spatial prediction approach produced the best predictive surface for dreissenid biomass in Lake Erie. The estimated spatial pattern of dreissenid biomass may be used in nutrient modeling exercises across Lake Erie to improve estimates of dreissenid impacts on biogeochemistry and lower food web productivity. This work also provides insight for potential sampling design improvements for benthic surveys in future years that could help refine the predictive capacity of the developed spatial prediction framework.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".