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Record W4281713547 · doi:10.1071/aj21461

Panel discussion: The shape of things to come

2022· article· en· W4281713547 on OpenAlexaff
Ali Moore, Tony Nunan, Kahina Abdeli-Galinier, Kevin Gallagher, Patrick G. Hartley, Iman Hill

Bibliographic record

VenueThe APPEA Journal · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Policies
Canadian institutionsWorld Federation of Science Journalists
Fundersnot available
KeywordsPanel discussionTransformation (genetics)BusinessEmerging technologiesMarketingTelecommunicationsEngineeringComputer scienceAdvertising

Abstract

fetched live from OpenAlex

As technology and innovation will continue to underpin the industry’s growth whilst reducing emissions and continuing to meet our energy needs, we explore the technologies that are driving this transformation. To view the video, click on the link to the right.

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)0.007
metaresearch head score (Gemma)0.012
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.078
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0120.003
Scholarly communication0.0090.008
Open science0.0020.006
Research integrity0.0230.027
Insufficient payload (model declined to judge)0.0780.019

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 head0.014
GPT teacher head0.224
Teacher spread0.209 · 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
GenreCommentary

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
Published2022
Admission routes1
Has abstractyes

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