Mogelijkheden om monitoring van klimaatimpact van de landbouw te verbeteren via sectordata : Adviesnotitie aan Taakgroep Landbouw vanuit de PPS Klimaatperspectief
Bibliographic record
Abstract
Deze notitie gaat in op mogelijkheden om de (toekomstige) monitoring van de klimaatimpact van de Nederlandse landbouw te verbeteren door gebruik te maken van data van het bedrijfsleven.Dit soort verbeteringen zijn belangrijk om effecten van emissie-reducerende maatregelen mee te kunnen tellen in de landenrapportage richting de Verenigde Naties.Het realiseren van de geïdentificeerde verbeterpunten vereist structurele afstemming en samenwerking tussen overheid en bedrijfsleven.Als dit niet haalbaar blijkt, ontstaat of een risico op een dubbel registratiesysteem of een risico dat inspanningen van de landbouw onzichtbaar blijven.Deze notitie is opgesteld in het kader van de PPS Klimaatperspectief en richt zich tot de Taakgroep Landbouw omdat binnen deze groep besluiten worden genomen om bestaande uitgangspunten en berekeningen ten aanzien van emissies uit de landbouw aan te passen.This note discusses possibilities to improve (future) monitoring of the climate impact of Dutch agriculture by using data from the private sector.Such improvements are important to be able to take effects of emissionreducing measures into account in the national report towards the United Nations.Realising the identified improvement opportunities requires structural coordination and cooperation between government and industry.If this does not prove feasible, there is either a risk of a double registration system or a risk that mitigation efforts by agriculture will remain invisible.This note was composed within the PPP Climate Perspective and is addressed to Taakgroep Landbouw (Task Force Agriculture).This taskforce takes decisions to adjust existing assumptions and calculations regarding emissions from agriculture.
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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 | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".