Integrating resource recovery process and watershed modelling to facilitate decision-making regarding bio-fertilizer production and application
Bibliographic record
Abstract
Abstract Waste management strategies such as anaerobic digestion and composting produce bio-based fertilizer products that could be applied to agricultural soil. Although multiple modelling software tools are available to simulate the environmental effect of fertilizer application to the soil, these models do not allow specification of emerging bio-based fertilizer types. Moreover, mathematical process models exist that allow optimizing the operational settings of waste management processes in order to produce an optimal bio-fertilizer quality adjusted to the local market needs. If an integrated tool would be available that couples process modelling to watershed modelling, the valorization chain could be simulated as a whole, i.e. the bio-fertilizer type and composition could be adjusted to the local watershed and environmental impacts of bio-based fertilizer production and application could more easily be assessed and controlled. The availability of such integrated tool may as such allow for improved decision and policy making regarding bio-fertilizer production and application with environmental benefits as a result.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".