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Record W4402099505 · doi:10.1093/biosci/biae070

Going global by going local: Impacts and opportunities of geographically focused data integration

2024· article· en· W4402099505 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioScience · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsUniversity of Alberta
FundersCommonwealth Scientific and Industrial Research OrganisationAustralian Government
KeywordsGeographyRegional scienceEnvironmental resource managementEarth scienceEconomic geographyEnvironmental planningEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Abstract Biodiversity conservation is a global challenge that requires the integration of global and local data. Expanding global data infrastructures have opened unprecedented opportunities for biodiversity data storage, curation, and dissemination. Within one such infrastructure—the Global Biodiversity Information Facility (GBIF)—these benefits are achieved by aggregating data from over 100 regional infrastructure nodes. Such, regional biodiversity infrastructures benefit scientific communities in ways that exceed their core function of contributing to global data aggregation, but these additional scientific impacts are rarely quantified. To fill this gap, we characterize the scientific impact of the Atlas of Living Australia, one of the oldest and largest GBIF nodes, as a case study of a regional biodiversity information facility. Our discussion reveals the multifaceted impact of the regional biodiversity data infrastructure. We showcase the global importance of such infrastructures, data sets, and collaborations.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.081
GPT teacher head0.341
Teacher spread0.260 · 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