Methodes statistiques de prevision de l'evolution d'une chaussee
Bibliographic record
Abstract
This study conducted as a joint undertaking by the LCPC laboratory and the Québec Transportation Ministry's Roads Laboratory Division (DLC/MTQ), has enabled evaluating various methods for modeling pavement evolution, in particular by esting their applicability to the data stemming from a test-section monotoring program. Three methods have been considered herein ; they have been derived from : 1) association of an iterative adjustment process with a multilinear regression ; 2) application of "survival laws" theory; and 3) non linear regression. Each of these three methods is discussed in depth and it will be shown both how and to what extend they enable identifying the set of variables that govern pavement behavior (the so-called "explanatory" variables). These methods then get applied to data yielded from the Quebec Transportation Ministry's test section monitoring program in order to model the evolution of thermal shrinkage cracking on flexible pavements (i.e. pavements containing thick bituminous layers). This study has demonstrated both the importance and difficulty involved in identifying the explanatory variables behind the trends encountered during modeling. To overcome at least in part this difficulty, a so-called "robust" section hypothesis has been introduced ; its purpose is intended to incorporate the explanatory variables heretofore unidentified and hence correlate with a greater level of precision the behavior of a given family with that of a specific section in this family.
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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 | 0.024 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".