Dynamic Waste Management (DWM): A new step towards industrial ecology
Bibliographic record
Abstract
The load caused by growing volumes of collected residues produces a pressure both on the environment and on the population.Presently, wastes are no longer considered just simple residues, but also resources which can be reused or recovered.Various tools are now available to help compare the various options in waste management.These tools lead to a linear management of the systems.In addition, they generate a division of flow streams and do not allow an optimal use of the available materials.An industrial ecology perspective requires one to move towards a systemic approach.Based on the law of conservation of energy, the Dynamic Waste Management approach (DWM) studies the behaviour of systems to insure a constant supply of favourable waste management options (WMO) and to reduce the global load within these systems.The flow distribution in a global system rests on the principle that the available volumes and the transportation influence the validity of the hierarchical organisation of WMO.Adhering to the philosophy of industrial ecology, the DWM offers a dynamic and evolutionary alternative to reduce the impacts generated by inadequate waste management.The present article exposes the characteristics of the proposed approach, and presents an example of its application in order to demonstrate the advantages of the DWM.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".