Utilisation de l'écologie industrielle et de l'intelligence économique territoriale pour le développement durable d'une zone industrialo-portuaire
Bibliographic record
Abstract
This paper presents the issue and the methodology of an ongoing competitive and territorial intelligence approach of the industrial and harbour area of Fos-sur-Mer (Bouches-du-Rhône, France). It aims knowing this territory, to make it more competitive, while respecting the populations and the environment.The originality of the method is to use the industrial ecology approach as a tool for the competitive intelligence. The first results show that industrial ecology and competitive intelligence are complementary tools to support the sustainable development of an industrial area. Une approche d'intelligence économique territoriale a été mise en place au sein de la zone industrialo-portuaire (ZIP) de Fos-sur-Mer, à l'initiative du Port autonome de Marseille (PAM), en 2004. Elle vise à mieux connaitre ce territoire pour le rendre plus compétitif et diversifier ses activités, dans le respect des populations et de l'environnement. L'originalité de la méthode est d'utiliser l'écologie industrielle comme outil d'intelligence économique territoriale.L'objectif de cette étude est d'assurer un développement durable de la ZIP et son insertion dans le tissu urbain, industriel et naturel existant. Les premiers résultats y sont présentés et montrent que l'écologie industrielle et l'intelligence économique et territoriale sont des outils complémentaires pour soutenir le développement durable d'une zone d'activité.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".