Spatialization in LCA. Interests, feasibility and limits of eco-design. EcoSD Annual Workshop 2017
Bibliographic record
Abstract
/ The EcoSD network is a French association whose main objective is to encourage collaboration between academic and industrial researchers so they may create and spread advanced and multidisciplinary knowledge in the eco-design fields at national and international levels. EcoSD proposes several actions with the support of the French Environment and Energy Management Agency (ADEME), the French Ministry of Higher Education and Research, and the French Ministry of Industry : Structuring EcoSD research activities in France to take advantage of the expertiseof more than 200 members of this research network Developing knowledge among researchers in the eco-design fields, particularly better training of Ph.D. students, by organizing relevant training courses for different themes in eco-design Developing new methods, tools and databases to achieve complex systems design, compatible with the principle of sustainable development Initiating the "EcoSD label" to acknowledge the quality and inclusion of sustainable development in training, research programs, research projects and symposiums Helping interactive collaboration between researchers and industrial partners byorganizing quarterly research seminars in Paris and an annual workshop. \nApproximately 100 researchers from industry, academia and government institutions participated in the 2017 workshop on "Spatialization in LCA" and had the opportunity to exchange with experts. The associated publication contains a synthesis of the main contributions presented during this workshop The objectives of the workshop were to cross different visions, methods and case studies gathering the most recent researches in France, Canada and Luxembourg. \nResearchers were invited to present their work in various application fields : agricultural, construction, waste management and urban planning. An industrial from energy sector and a regional authority were also invited to explain how they consider spatial information in their actual practices. This book contains a synthesis of the main contributions presented during this workshop divided into three sections : existing and advanced tools, integrated approach, and, perspectives for spatialization in LCA.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".