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Record W4405773808 · doi:10.1021/acssuschemeng.4c08902

Towards the Sustainable Management of Chemicals and Waste: Weaving Indigenous Knowledge with Green and Sustainable Chemistry

2024· article· en· W4405773808 on OpenAlexaff
James M. Ataria, Te Kaurinui Parata, Audrey Moores, Huia Iti, Christopher Hill, Susan Chiblow, Michelle Murphy, Deborah McGregor, Bradley Moggridge, Daniel Hikuroa, Louis A. Tremblay, Gunilla Öberg, Marc Demers, Bryan W. Brooks

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsEnvironment and Climate Change CanadaRed River CollegeUniversity of British ColumbiaYork UniversityUniversity of GuelphCentre in Green Chemistry and CatalysisMcGill UniversityUniversity of TorontoAssembly of First Nations
FundersMinistry of Business, Innovation and EmploymentRoyal Society Te Apārangi
KeywordsWeavingGreen chemistryIndigenousContext (archaeology)SustainabilitySustainable developmentTraditional knowledgeChemistryEngineeringEngineering ethicsNanotechnologyPolitical scienceOrganic chemistryMaterials scienceGeographyEcologyCatalysisArchaeology

Abstract

fetched live from OpenAlex

The Ma̅ori concept of whakapapa is introduced as an opportunity to understand where and how chemical feedstocks are sourced, within a broader context of weaving Indigenous knowledge with green and sustainable chemistry.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.002
GPT teacher head0.175
Teacher spread0.173 · 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 designBench or experimental
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

Citations5
Published2024
Admission routes1
Has abstractyes

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