MétaCan
Menu
Back to cohort
Record W2940456088 · doi:10.1016/j.eng.2019.03.004

The Deep Carbon Observatory: A Ten-Year Quest to Study Carbon in Earth

2019· article· en· W2940456088 on OpenAlexfundno aff
Craig M. Schiffries, Andrea Johnson Mangum, Jennifer Mays, Michelle Hoon‐Starr, Robert M. Hazen

Bibliographic record

VenueEngineering · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
FundersHorizon 2020Japan Society for the Promotion of ScienceJapan Agency for Marine-Earth Science and TechnologyNatural Environment Research CouncilCanadian Space AgencyEuropean Research CouncilConseil Régional, Île-de-FranceU.S. Department of EnergyEuropean CommissionChinese Academy of SciencesCarnegie Institution of WashingtonDeutsche ForschungsgemeinschaftNatural Sciences and Engineering Research Council of CanadaAlfred P. Sloan FoundationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungCanada Research ChairsMinistry of Education and Science of the Russian FederationMinistry of Education, Culture, Sports, Science and TechnologyNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsObservatoryAstrobiologyEarth (classical element)Carbon fibersEnvironmental scienceGeologyEarth scienceAstronomyComputer sciencePhysics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.005
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.006
Open science0.0010.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.003
GPT teacher head0.167
Teacher spread0.164 · 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
GenreReview

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

Citations3
Published2019
Admission routes1
Has abstractno

Explore more

Same venueEngineeringSame topicAtmospheric and Environmental Gas DynamicsFrench-language works237,207