Bibliographic record
Abstract
Seasonal variation of temperature and moisture causes considerable changes in the load-carrying capacity of pavements in geographical areas subjected to extreme freeze/thaw conditions. The Seasonal Monitoring Program (SMP) of the Long Term Pavement Performance (LTPP) study of the Federal Highway Administration (FHWA) monitors seasonal variations in Falling Weight Deflectometer (FWD) deflections, air temperature, rainfall, soil temperature, moisture content, and soil electrical resistance at numerous sites across North America. This study relates changes in pavement load carrying capacity represented by the pavement layer resilient moduli to selected environmental factors for a test pavement site in southwestern Manitoba. The significant environmental parameters causing seasonal variation in pavement layer resilient moduli are identified as the surface temperature for the asphalt layer and the thawing index for base and subgrade layers. The resilient moduli of various pavement layers show a substantial decrease in magnitude with increasing values of respective environmental parameter. The present model can be used for preliminary verification of empirical pavement design and rehabilitation practices currently in use to account for seasonal variations. Key words: asphalt, backcalculation, falling weight deflectometer, freezing, pavements, moisture, resilient moduli, seasonal variation, thawing, temperature.
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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 | 0.000 | 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) | 0.022 | 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".