Hortifootprint Category Rules : Towards a PEFCR for horticultural products
Bibliographic record
Abstract
The demand of consumers and retailers for sustainably produced horticultural products is increasing. Life Cycle Assessment (LCA), or environmental footprint analysis, is a widely acknowledged methodology to assess, benchmark and monitor the environmental impact. Therefore, all supply chain partners are increasingly asking for footprint calculations of horticultural products. Because no harmonised methodology is available, the footprint calculations based on various methodological choices make those difficult to interpret. The project is carried out in the framework of a Public-Private Partnership project called ‘Methodology for environmental footprint’. This report delivers the set of methodological rules for calculating the environmental footprint of horticultural products and is primarily meant for professionals with moderate knowledge of LCA. The development of the methodology follows as much as possible the most recent Guidance for developing Product Environmental Category Rules (PEFCR) published by the European Commission. This Hortifootprint Category Rules guidance suggests a ‘flexible’ approach, giving practitioners flexibility to 1) define the system boundaries of the study to be performed and 2) select the secondary data to be used as background to model the different life cycle stages in scope. Although this flexibility makes HFCR suitable for a broader set of use cases, it has as a drawback that it only allows for comparisons within the same study, provided that the same background data and scope definition are chosen.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.042 |
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".