Albedo of Pavement Surfacing Materials: In Situ Measurements
Bibliographic record
Abstract
In order to limit heat intake in the ground underneath pavements, high albedo surfaces can be used in cold regions to mitigate permafrost degradation. In this study, experimental sections at Site Experimental Routier de l’Université Laval (Québec, 2014), on the Alaska Highway (Beaver Creek, Yukon, 2012 and 2014 and at km post 1786, Yukon, 2014), and in Tasiujaq (Nunavik, Québec, 2015) were used to document the effectiveness and durability of high albedo surfacing materials. The test sections were equipped with thermistors and data loggers recording surface temperatures. Albedo and skid resistance were also monitored at these sections. In addition to the experimental sites, several albedo measurements were made on asphalt surfaces to develop a relationship between albedo and pavement age.
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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 | 0.001 | 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) | 0.002 | 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".