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Record W3096352605 · doi:10.1126/sciadv.abb9103

Aerial strategies advance volcanic gas measurements at inaccessible, strongly degassing volcanoes

2020· article· en· W3096352605 on OpenAlexaff
Emma Liu, Alessandro Aiuppa, Alfredo Alan, Santiago Arellano, Marcello Bitetto, Nicole Bobrowski, Simon Carn, R.J. Clarke, Ernesto Corrales, J. Maarten de Moor, J. A. Diaz, Marie Edmonds, Tobias P. Fischer, Jim Freer, G. Matthew Fricke, B. Galle, Gustav Gerdes, Gaetano Giudice, A. Gutmann, Catherine Hayer, Ima Itikarai, J. G. Jones, Emily Mason, Brendan McCormick Kilbride, Kila Mulina, Scott Nowicki, K. E. Rahilly, Thomas Richardson, Julian Rüdiger, C. Ian Schipper, I. M. Watson, Kieran Wood

Bibliographic record

VenueScience Advances · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsCanmore Museum and Geoscience Centre
FundersSvenska Forskningsrådet FormasHorizon 2020Engineering and Physical Sciences Research CouncilRoyal Society Te ApārangiLeverhulme TrustAlfred P. Sloan FoundationSwedish National Space AgencyNational Science Foundation
KeywordsVolcanoMeasure (data warehouse)Volcanic GasesRemote sensingGeologyEnvironmental scienceEarth scienceSeismologyComputer scienceData mining

Abstract

fetched live from OpenAlex

flux and enable near-real-time measurements of plume chemistry and carbon isotope composition. Our data emphasize the need to account for time averaging of temporal variability in volcanic gas emissions in global flux estimates.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.019
GPT teacher head0.252
Teacher spread0.234 · 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 designObservational
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

Citations58
Published2020
Admission routes1
Has abstractyes

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