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Record W4378781601 · doi:10.1089/ast.2022.0071

NoRCEL's Engagement in Africa: The AstroScience Exploration Network (ASEN)

2023· article· en· W4378781601 on OpenAlexaff
Sohan Jheeta, M. Dominik, Elias Chatzitheodoridis, O. R. Kotsyurbenko, Miryam Palacios-Pérez, Sávio Torres de Farías, Kathy McGrath, Ahya Rezaei, Mukesh Chiman Bhatt, Richard Gordon

Bibliographic record

VenueAstrobiology · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace Science and Extraterrestrial Life
Canadian institutionsUniversity of ManitobaNortel (Canada)
Fundersnot available
KeywordsAsset (computer security)Variety (cybernetics)Key (lock)FunnelBusinessPolitical scienceEconomyComputer scienceEngineeringComputer securityEconomicsArtificial intelligence

Abstract

fetched live from OpenAlex

The AstroScience Exploration Network (ASEN) is the latest innovative initiative from the Network of Researchers on the Chemical Emergence of Life (NoRCEL). Materializing on the vibrancy of the African continent, recognizing its people as a key asset, and building on specific strategic advantages, ASEN will funnel the appetite for scientific knowledge through an educational hub that paves the way for the Global South to come to the fore in new global endeavors and will eventually help build a variety of career paths in a diversifying economy.

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)0.002
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0030.003
Open science0.0000.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0210.002

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 head0.063
GPT teacher head0.283
Teacher spread0.220 · 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
Published2023
Admission routes1
Has abstractyes

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