Samfunnsøkonomisk analyse av halvering av matsvinn i henhold til bransjeavtalen om redusert matsvinn – Klimakur 2030
Bibliographic record
Abstract
Dette notatet omhandler tiltak for redusert matsvinn i forbindelse med klimakur 2030. Datagrunnlaget har blitt forbedret sammenlignet med tidligere utredninger av dette tiltaket. Det gjelder først og fremst en produktspesifikk relativ fordeling av «redusert matsvinn». Bransjeavtalen om redusert matsvinn ligger til grunn for analysen. Basert på beregningene og forutsetningene i analysen, vil tiltaket føre til en total utslippsreduksjon i norsk landbruk tilsvarende 1 952 000 tonn CO2-ekvivalenter. Tiltakskostnaden er beregnet til -9 753 kr per tonn CO2-ekvivialent. Tiltaket har en negativ kostnad per tonn utslippsreduksjon, hvilket betyr at samfunnet samlet sett vil spare både penger og bidra til å redusere klimagassutslippene fra norsk landbruk gjennom tiltaket. Tiltaket vil i tillegg bidra til utslippskutt i andre sektorer og land.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.015 |
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".