Bibliographic record
Abstract
The paper describes a synthesis of works conducted by the author in the field of high performance concretes and road skid resistance. Conducted as part of a PhD thesis at the University of Sherbrooke (Canada), the research on the fatigue behavior can characterize high performance concretes - both in terms of materials and structural elements - and compared with normal concrete. The analysis of fatigue life using concepts in reliability can calculate failure probability, indicator more relevant than the average fatigue life. Road skid resistance is ¾ of the research of the author, as a researcher and then team leader and project . The areas covered range of influential factors of skid resistance, including the road surface microtexture, to use the knowledge of available skid resistance - especially when it is degraded - to warn the driver, including also important aspects like the skid resistance evolution with the traffic and the European harmonization of measurement methods. This gradual progression help to deal with skid resistance from its cognitive (understanding, modeling) to applications, and to tackle with the laboratory-to-road transition and the scale change issues. This career of 20 years allowed the author to capture different aspects of research coordination such as project proposals, the development of partnerships - with a strong emphasis on international collaborations, supervision of researchers or management of a laboratory. Independent at first, these bricks were then structured to build a methodology of research coordination. Research perspectives for the future (4-5 years) are also identified to fill perceived gaps in this synthesis.
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 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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".