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Record W3049374144 · doi:10.1080/00206814.2020.1802782

Carbon dioxide emission from monogenetic volcanoes in the Mt. Changbai volcanic field, NE China

2020· article· en· W3049374144 on OpenAlexaff
Yutao Sun, Zhengfu Guo, Danielle Fortin

Bibliographic record

VenueInternational Geology Review · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Ottawa
FundersNatural Science Foundation of Hebei ProvinceNational Natural Science Foundation of China
KeywordsVolcanoCalderaGeologyMagmaLava fieldGeochemistryLavaSeismologyEarth science

Abstract

fetched live from OpenAlex

A systematic investigation along with field measurements were performed at the Mt. Changbai volcanic field on the border between China and North Korea from June to September in 2016, with respect to geological sources of degassed CO2 and its relationship with fluids between the monogenetic and polygenetic volcanoes. Our estimates indicate that the flux of CO2 degassing from the polygenetic and monogenetic volcanoes in the Mt. Changbai volcanic field is 1.8 × 105 t/yr, which corresponds to the middle level of CO2 output from volcanic fields in the world. The flux of CO2 degassing from the soil shows a decreasing trend with distance from the Tianchi polygenetic volcanic caldera. Temporal variations of contrast value in the Tianchi polygenetic volcanic caldera and peripheral areas are more conformable than those in the monogenetic volcanoes. Based on the analysis to the soil CO2 fluxes, we propose that monitoring CO2 fluxes in the polygenetic volcanic caldera and peripheral areas would provide useful information about the local volcanic activity, especially in dormant volcanoes like the Tianchi polygenetic volcanic caldera. Other CO2 flux measurement sites not only related to the Tianchi caldera area should also be selected for future volcanic monitoring.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.000

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 head0.017
GPT teacher head0.228
Teacher spread0.210 · 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 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

Citations14
Published2020
Admission routes1
Has abstractyes

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