Bibliographic record
Abstract
With the advent of the industrial revolution, greenhouse gases have been emitted into the atmosphere causing the alteration of weather across the globe. These gases have caused the global temperature to warm. This has also caused a change in precipitation, wind speeds, and wave heights. The Great Lakes have many cities located on their shores and millions of people are affected by waves. Effects include shoreline erosion, infrastructure damage, and loss of valuable property and lives. It is imperative to future planning and engineering that the trends of wave height are understood. This study uses linear regression, a well-known and easily understood method, to analyze all the Great Lakes’ wave heights, moving average, moving standard deviation, and 100-year recurrence value. Nineteen wave buoys were selected for analysis, encompassing all the Great Lakes. It was found that some locations have increasing wave height trends while other locations have decreasing trends. Five stations had positive trends for both storm magnitude and standard deviation. Five stations had one positive and one negative trend. Nine stations had decreasing storm average and standard deviations. The 100-year wave height value is also increasing at 11 stations and decreasing at 8 stations. This goes to show that waves are changing, but in different ways for different station locations. Planners can use this information to plan future erosion and infrastructure activities and budgets.
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.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".