Case history on the design, implementation, and conclusions of a study of haul roads associated with a wind farm development in Southwestern Ontario
Bibliographic record
Abstract
A wind farm consisting of 72 turbines is to be constructed in the Town of Lakeshore, in Southwestern Ontario. For the construction, turbine components will be hauled on a network of rural low-volume roads. When these roads were designed and built, their use for the heavy haul required by the wind farm development was never envisaged. Consequently, the Town of Lakeshore commissioned a baseline study of the roads under their jurisdiction identified on the proposed haul routes. A total of 55 road segments, with gravel, surface-treated (chip-sealed) and asphalt surfaces was examined. The study included visual pavement condition assessment, falling weight deflectometer (FWD) testing, photographic work, and video recording. The following are covered in the paper: the considerations that went into the design of a cost-efficient, successful field testing program; the methods used for the pavement condition survey; the arrangement of the FWD study; the results of the pavement condition survey; the results of the FWD study; and recommendations for follow-up study of the haul roads. For the covering abstract of this conference see record contro number 201111RT334E.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".