Details on the Evaluation of Sampling Procedures for Detecting Potato Wart Infection in Fields:Notat om tema 2: Vurdering af prøvetagningsmetode og -intensitet af kartoffelbrok til brug for afregulering af tidligere smittede marker
Bibliographic record
Abstract
NaturErhvervstyrelsen Vedrørende "Notat om tema 2: Vurdering af prøveudtagningsmetode og -intensitet af kartoffelbrok til brug for afregulering af tidligere smittede marker" NaturErhvervstyrelsen (NAER) anmodede med bestilling af 30.marts 2015 DCA -Nationalt Center for Fødevarer og Jordbrug (DCA) om en vurdering af smittetrykkategorier, karenstider og prøvetagningseffektivitet ved udbrud af kartoffelbrok.NAER anmodede dels om en vurdering af NAERs generelle retningslinjer for opdeling af smittetryk af kartoffelbrok og efterfølgende afvikling af karenstid (Tema 1), dels om en vurdering af risikoen for falsk-negative bedømmelser ved forskellige prøvetegningsintensiteter (Tema 2).DCA fremsendte den 17. april 2015 "Notat vedrørende NaturErhvervstyrelsens generelle retningslinjer for kategorisering af smittetryk af kartoffelbrok og de tilhørende restriktioner" som svar på bestillingens Tema 1. Som besvarelse på bestillingens Tema 2: "Prøvetagningseffektivitet" fremsendes hermed vedlagte "Notat om tema 2: Vurdering af prøveudtagningsmetode ogintensitet af kartoffelbrok til brug for afregulering af tidligere smittede marker".Notatet er udarbejdet af seniorforsker Rodrigo Labouriau, Institut for Matematik, og seniorforsker Bent J. Nielsen, Institut for Agroøkologi.Det er led i "Aftale mellem Aarhus Universitet og Fødevare-ministeriet om udførelse af forskningsbaseret myndighedsbetjening m.v.ved
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".