Municipal bio-waste can be recovered through various technologies. Some may be industrial-scale, others may be neighborhood-scale or even individual. Moreover, geographical, demographic and socio-economic characteristics present a high degree of territorial variability. The intersection of these technological and socio-economic diversities leads us to define two meta-scenarios for the management of these bio-waste: either decentralized management that avoids costly collection but leads to low added value co-products, or centralized management that requires collection and transport of bio-waste to an industrial recovery unit but leads to higher added value co-products. Both meta-scenarios reveal environmental and economic tensions, coupled with social considerations. Finally, pending a circular economy, it is necessary to verify the circularity of economic and material flows within the territory. As a result, the management scenarios must be adapted to the specific characteristics of the territory and assessed environmentally, economically and socially. The life cycle analysis provides a comprehensive assessment of these impacts. In the context of a circular economy, it is also necessary to analyze and review the economic models of the sector and the structural relationships between players. These objective elements of analysis are intended to help decision-makers choose the appropriate system for managing municipal bio-waste on a territory in a logic of circular economy.
Bibliographic record
Abstract
Municipal bio-waste can be recovered through various technologies. Some may be industrial-scale, others may be neighborhood-scale or even individual. Moreover, geographical, demographic and socio-economic characteristics present a high degree of territorial variability. The intersection of these technological and socio-economic diversities leads us to define two meta-scenarios for the management of these bio-waste: either decentralized management that avoids costly collection but leads to low added value co-products, or centralized management that requires collection and transport of bio-waste to an industrial recovery unit but leads to higher added value co-products. Both meta-scenarios reveal environmental and economic tensions, coupled with social considerations. Finally, pending a circular economy, it is necessary to verify the circularity of economic and material flows within the territory. As a result, the management scenarios must be adapted to the specific characteristics of the territory and assessed environmentally, economically and socially. The life cycle analysis provides a comprehensive assessment of these impacts. In the context of a circular economy, it is also necessary to analyze and review the economic models of the sector and the structural relationships between players. These objective elements of analysis are intended to help decision-makers choose the appropriate system for managing municipal bio-waste on a territory in a logic of circular economy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".