Monitor voortgang verduurzaming voedselketens : Droge kruidenierswaren
Bibliographic record
Abstract
In dit rapport wordt op de volgende onderdelen gekeken naar de verduurzaming van ketens voor droge kruidenierswaren: energieverbruik, gewasbeschermingsmiddelen, bemesting, waterverbruik, landtransformatie, gezondheid en veiligheid van werknemers, arbeidsrechten, traceerbaarheid, toegang tot inputs en markten voor kleinschalige boeren, en achteruitgang van bestuivers.Voor Nederlandse agrarische grondstoffen is de voortgang op het gebied van milieu de afgelopen jaren beperkt.De doelen voor gewasbeschermingsmiddelen en bemesting zijn niet gehaald, het waterverbruik is toegenomen en het energieverbruik in de teelt is niet gedaald.In de verwerking is het energieverbruik wel gedaald.Voor grondstoffen uit andere werelddelen is beperkt informatie beschikbaar.Wel neemt het aandeel van het verbruik dat duurzaam gecertificeerd is toe.This report assesses the sustainability of supply chains for dry goods based on the following factors: energy consumption, crop protection agents, fertiliser, water use, land transformation, employee health and safety, worker rights, traceability, access to inputs and markets for small-scale farmers, and the decline of pollinators.For agricultural raw materials in the Netherlands, environmental progress has been limited in recent years.Targets for crop protection agents and fertilisers have not been achieved, water use has increased and there's been no drop in energy consumption for crop production.There has, however, been a decrease in energy consumption by processors.Limited information is available on raw materials from other parts of the world, but the proportion of products certified as sustainable has increased.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.036 |
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".