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Record W7006314283

Task Forcen for tyndt befolkede områder under EK-NER : Mandat for Task Forcen for tyndt befolkede områder (TBO) - 2007.

2007· article· da· W7006314283 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2007
Typearticle
Languageda
FieldSocial Sciences
TopicLand Use and Management
Canadian institutionsnot available
Fundersnot available
KeywordsTask (project management)Task forceField (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

Nordisk Ministerråd har i 2006 understreget behovet for en tydelig langsigtet indsats for udbredelsen af vedvarende energi, også i de tyndt befolkede områder.Endvidere ønsker de nordiske energiministre, at der sættes fokus på samarbejdet med Nordens naboer mod vest i Canada og på Shetlandsøerne.Forhistorien hertil er, at Nordisk Ministerråd for Energi på møde i Gøteborg den 30. september 2003 besluttede at gøre særskilt opmærksom på og støtte indsatser til fremme af en holdbar energiforsyning i tyndt befolkede områder og i Arktis.Endvidere tilkendegav Nordisk Ministerråd for Energi og besluttede på møde i Narsarsuaq den 8. august 2005 at undersøge forudsætningerne for et udvidet samarbejde omkring energiforsyning til også at omfatte nabolandene som Canada og Shetlandsøerne.Embedsmands Kommitteen for Energi, Næring og Regional (forkortes EK-NER) har derfor nedsat en Task Force for bæredygtig energiforsyning i tyndt befolkede områder. Task Forcens betegnelse er i daglig tale TBO.

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)0.010
metaresearch head score (Gemma)0.011
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.095
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0030.006
Research integrity0.0080.005
Insufficient payload (model declined to judge)0.0720.045

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 head0.031
GPT teacher head0.326
Teacher spread0.295 · 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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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