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Record W4313528549 · doi:10.1021/cen-10101-buscon9

Svante raises funds for carbon capture

2023· article· en· W4313528549 on OpenAlexaboutno aff
Craig Bettenhausen

Bibliographic record

VenueC&EN Global Enterprise · 2023
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsFlue gasWaste managementNatural gasChevron (anatomy)TonneSorbentCarbon fibersEnvironmental scienceSan JoaquinEngineeringBusinessNatural resource economicsEconomicsChemistryMaterials scienceGeology

Abstract

fetched live from OpenAlex

The carbon-capture-technology firm Svante has raised $318 million in a series E funding round led by Chevron. Svante says it will invest the money in its facility in Vancouver, which is designed to produce enough sorbent modules to capture 1 million metric tons of carbon dioxide per year. The firm is targeting industrial sources of CO 2 , such as chemical plants and factories making hydrogen, paper, cement, and metals. Svante is also exploring direct air capture of CO 2 . Chevron plans to start testing Svante’s process on a natural gas combustion flue stream in California’s San Joaquin Valley in the next few weeks.

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)0.008
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: Other
Teacher disagreement score0.153
Threshold uncertainty score0.513

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0030.001
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.1530.055

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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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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