Temporal Analysis of Conditions in the Great Lakes using Data from Buoys in Lake Erie
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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 it