Temporal Analysis of Conditions in the Great Lakes using Data from Buoys in Lake Erie
Bibliographic record
Abstract
Climate change will have an impact on regional winds in Southern Ontario, which in turn will impact waves and currents in the Great Lakes. Additional impacts of these changes can include increased coastal erosion, degradation of nearshore ecology, damage to local fisheries and increased natural hazards such as heavy flooding and increased intensity of rip currents. Through analyses of wind speed and wind direction data from fourteen NOAA buoys in Lake Erie, average monthly northerly and easterly vector data was generated for each year a buoy had been in operation. The monthly vector data was transformed into charts to display the temporal patterns of the buoys in the lake. Temporal ranges of some of the buoys date back to 1980, providing long-term data to compare with conditions of today. The data in conjunction with spatial analysis tools such as GIS could give us insights into locations in the Lakes that are at highest risk for consequences of climate such as coastal erosion and flooding. The temporal data can also help us pinpoint times and areas of extreme events. This analysis can help inform what we may see in the future of climate change and provide a basis for policy decisions and protective actions along the Great Lakes and other large fresh water bodies in the world.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".