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Record W1520969108 · doi:10.22230/src.2010v1n1a1

An Open Access Approach to Scientific Information Management at the Agricultural Research Corporation

2009· article· en· W1520969108 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueScholarly and Research Communication · 2009
Typearticle
Languageen
FieldComputer Science
TopicInformation Science and Libraries
Canadian institutionsnot available
Fundersnot available
KeywordsCorporationDisseminationAgricultureContext (archaeology)BusinessKnowledge managementState (computer science)Public relationsPolitical scienceComputer scienceTelecommunicationsGeography

Abstract

fetched live from OpenAlex

Abstract: This article presents the experience of the Brazilian Agricultural Research Corporation (Embrapa) – a large state-owned company that plays an important global role in research, development, and innovation for tropical agriculture – in the planning and implementation of Open Access to scientific information in the context of a developing country. The aim of this initiative is to provide the necessary mechanisms to capture, store, organize, preserve, retrieve, and widely disseminate the scientific information produced by Embrapa and by agricultural research communities. This report concludes with a discussion of the obstacles encountered and the organizational features, cultural considerations, and political matters that facilitate open access implementation at Embrapa.

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.

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)0.018
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Open science
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0070.000
Scholarly communication0.0700.097
Open science0.0100.006
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.254
GPT teacher head0.454
Teacher spread0.200 · 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