Stratégies logistiques et matières dangereuses
Bibliographic record
Abstract
ARGUMENTARY Companies' logistics strategies include supply and distribution choices and material storage policy choices, made in an often complex regulatory context, with multiple levels of government and, most of the time, a large number of stakeholders. To improve their profitability, companies are constantly seeking to maximize the efficiency of their supply chains. Those working with hazardous materials have an additional challenge: they must take into account the potential risks of these substances to workers, the public and the environment at each link in their chain. They must therefore integrate risk management into their logistics and operational decision-making processes. As the Lac-Megantic tragedy in Quebec, which occurred on July 6, 2013, painfully reminds us in terms of risk management related to hazardous materials, even the most improbable catastrophic scenarios can occur. TARGET The purpose of this book is to provide stakeholders in the field (transporters, producers, planners, legislators) as well as individuals who wish to learn more about hazardous materials issues with a set of essential information. The various chapters include, on the one hand, first-hand data and, on the other hand, a reflection on the various aspects of logistics strategies and the risks associated with the storage, handling and transport of hazardous materials. To buy the book, click here ARGUMENTAIRE Les strategies logistiques des entreprises incluent les choix d'approvisionnement et de distribution et les choix des politiques de stockage des matieres, choix effectues dans un contexte reglementaire souvent complexe, avec plusieurs paliers de gouvernement et, la plupart du temps, un grand nombre d'intervenants. Pour ameliorer leur rentabilite, les entreprises cherchent sans cesse a maximiser l'efficacite de leurs chaines logistiques. Celles qui oeuvrent avec des matieres dangereuses ont un defi supplementaire : elles doivent prendre en compte les risques potentiels de ces substances pour les travailleurs, la population et l'environnement, et ce, a chacun des maillons de leur chaine. Elles doivent donc integrer la gestion des risques a leur processus de decisions logistiques et d'operation. Comme nous le rappelle douloureusement la tragedie de Lac- Megantic au Quebec, survenue le 6 juillet 2013, en matiere de gestion des risques lies aux matieres dangereuses, meme les scenarios catastrophiques les plus improbables peuvent se produire. CIBLE Ce livre a pour but d'offrir aux intervenants du milieu (transporteurs, producteurs, planificateurs, legislateurs) ainsi qu'aux personnes qui souhaitent en savoir davantage sur les thematiques liees aux matieres dangereuses un ensemble d'informations indispensables. Les differents chapitres regroupent, d'une part, des donnees de premiere main et, d'autre part, une reflexion sur les divers aspects des strategies logistiques et les risques associes au stockage, a la manutention et au transport des matieres dangereuses. Pour acheter le livre, cliquez ici
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".