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Record W4386962009 · doi:10.18174/636580

Verkenning kosteneffectiviteit van potentiele technische innovaties voor de reductie van de ammoniakemissie : KBG NL Mest WP4 organisatie keten

2023· report· nl· W4386962009 on OpenAlexaff
K. Oltmer, H.H. Luesink, Tanja de Koeijer

Bibliographic record

Venuenot available
Typereport
Languagenl
FieldEnergy
TopicEnergy, Environment, Agriculture Analysis
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesPolitical scienceAgricultural sciencePhilosophyEnvironmental science

Abstract

fetched live from OpenAlex

Innovative techniques for manure processing and application can contribute to the reduction of ammonia emission from agriculture. For a number of techniques that are applied in dairy farming, pig production and veal calf farming the amounts of investment money, the annual costs and the reduction of the ammonia emission that can be achieved with the technique are assessed. The cost effectiveness is calculated as euro per kg NH3 per year. Furthermore, it is evaluated to what extent the techniques discussed are able to reach the targets concerning the ammonia emission reduction set by the Dutch government.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.468
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.005
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.027
GPT teacher head0.268
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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