Wind Conditions in a Fjordlike Bay and Predictions of Wind Speed Using Neighboring Stations Employing Neural Network Models
Bibliographic record
Abstract
Abstract This paper evaluates the applicability of neural networks for estimating wind speeds at various target locations using neighboring reference locations along the south coast of Newfoundland, Canada. The stations were chosen to cover a variety of topographic features and span distances in excess of 100 km. The goal of the study is to provide a general description of the summer wind conditions along the south coast of Newfoundland and to assess the potential application of neural networks for wind speed predictions. Analysis of wind data from July to October showed the wind going dominantly toward the northeast with speeds ranging from 0 to 45 m s −1 . The efficacy of neural networks to predict wind speeds varied among stations and was largely influenced by the presence/absence of wind barriers. Sensitivity analysis on neural network performance concluded that an absolute minimum of 3000 h of continuous monitoring is needed to effectively train neural networks to predict wind speeds. The conclusions of this study have implications for future work utilizing wind speed data where a generalization of uniform wind speeds is assumed.
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 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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".