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Record W4391777455 · doi:10.18174/649089

Actieagenda voor verbeterde kennisdoorstroming in de Nederlandse Tuinbouw

2023· report· nl· W4391777455 on OpenAlexaboutno aff
Ellen Bulten, H.B. Schoorlemmer

Bibliographic record

Venuenot available
Typereport
Languagenl
FieldEnvironmental Science
TopicSustainable Agricultural Systems Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

Het Nederlandse Agricultural Knowledge and Innovation System (AKIS) is sterk ontwikkeld, maar ook versnipperd. Er zijn veel partijen die parallel in het kennissysteem opereren met geen of beperkte coördinatie op sector, regionaal of landelijk niveau. Het gevolg is dat effectieve doorstroming van kennis gelimiteerd kan zijn. Kennis en innovatie zijn belangrijk in het (verder) verduurzamen van de Nederlandse land- en tuinbouw, daarom is het van belang dat de Nederlandse AKIS versterkt wordt en kennisdoorstroming wordt verbeterd. Op basis van een enquête, interactieve sessies en resultaten uit het EU project NEFERTITI is in deze studie een overzicht gemaakt van belangrijke barrières die kennisdoorstroming in de AKIS van de Nederlandse tuinbouw belemmeren en worden vervolgens aanbevelingen gedaan om gezamenlijk in actie te komen om deze kennisdoorstroming te verbeteren.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.225
Threshold uncertainty score0.448

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0080.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.002

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 head0.049
GPT teacher head0.303
Teacher spread0.254 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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