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Record W4233824901 · doi:10.33774/coe-2021-pk97p

Solution stars - Mitigation solutions for green recovery

2021· preprint· en· W4233824901 on OpenAlexaff
Agota Mockutė, Alessandra Zanoletti, Chloe Foster, Claire Holland, Emanuela Melis, Jason P. Hallett, Joan Marc Rodriguez Benuz, Jorge Sáinz, Mariangela Rosano, Milena Büchs, Ofonime Harry, Patricia Thornley, Sergey Kolesnikov, Wladmir Henriques Motta, Ying Zhou

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSustainable Industrial Ecology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSession (web analytics)Natural resourceStarsEnvironmental scienceNatural (archaeology)Computer sciencePolitical scienceGeographyWorld Wide WebArchaeology

Abstract

fetched live from OpenAlex

This rapid-paced Pecha Kucha session will see presenters from around the world propose, in 2 minutes, their mitigation solution for green recovery and technologies that might just provide the solution for a greener future. Topics will include bio-based solutions, using AI, decarbonisation and managing natural resources.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.785
Threshold uncertainty score1.000

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 integrity0.0010.000
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 head0.035
GPT teacher head0.242
Teacher spread0.207 · 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 designSimulation or modeling
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

Citations0
Published2021
Admission routes1
Has abstractyes

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