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Record W4386563824 · doi:10.1111/rec.14001

Creating new pathways for sharing knowledge to support restoration initiatives

2023· article· en· W4386563824 on OpenAlexaff
Gabriela Barragán, Cindy E. Prescott

Bibliographic record

VenueRestoration Ecology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPublicationPublic relationsSociology of scientific knowledgeBest practicePolitical scienceBusinessKnowledge managementSociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

In response to pressing global threats, nations worldwide have committed to restore vast areas of degraded land. But many nations, particularly in the Global South, are experiencing challenges in planning and managing these programs. Access to knowledge from the scientific literature is limited by barriers such as paywalls and language issues for non‐English speakers. Limited accessibility to the scientific literature can impair the ability of restoration managers and practitioners to apply scientific knowledge on the ground, jeopardizing local and global restoration efforts. We present the case of an education and training project Restaura Consciencia , which is a website featuring multilingual summaries of published scientific articles on restoration, conservation, and related disciplines. This not‐for‐profit academic project promotes the application of research findings and increases the visibility of the authors of articles, including often marginalized authors who publish in their native languages but want to share summaries in English and other languages. Restaura Consciencia promotes collective academic collaborations to support restoration initiatives.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.065
GPT teacher head0.286
Teacher spread0.221 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2023
Admission routes1
Has abstractyes

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