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Record W4401646452 · doi:10.5194/egusphere-2024-2502

Early engagement with First Nations in British Columbia, Canada: A case study for assessing the feasibility of geological carbon storage

2024· preprint· en· W4401646452 on OpenAlexafffundabout
Katrin Steinthorsdottir, Shandin Pete, Gregory M. Dipple, Richard Truman, Sandra Ó. Snæbjörnsdóttir

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsGeoscience BCUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTimelineWork (physics)PhoneCommunity engagementEnvironmental planningEnvironmental resource managementPolitical sciencePublic relationsEnvironmental protectionGeographyEngineeringEnvironmental scienceArchaeology

Abstract

fetched live from OpenAlex

Abstract. This work describes early engagement with 21 First Nations or alliances, that represent 41 Nations, in British Columbia, Canada. Geological researchers conducted this work as a case study to assess the feasibility of carbon storage in serpentinite rocks. The priorities for engagement were to inform people about the project and its implications, get consent for fieldwork, have a discussion, and start building relationships. Aside from the geology and logistics of a site for a carbon storage project, the permitting and acceptance by the local community and the traditional lands‘ rightsholders are needed for a successful project. The engagement levels and timelines varied from short phone calls to emails and video meetings. The general reception was positive, and people showed an interest and appreciated being contacted early. Common areas of discussion were water quality, salmon habitat, and involving the youth. This work outlines the first step for engagement, and further work will be done if a proposed CO2 storage project is to proceed.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.493

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0360.006
Scholarly communication0.0050.001
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.047
GPT teacher head0.309
Teacher spread0.262 · 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 designQualitative
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
Published2024
Admission routes3
Has abstractyes

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