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Research on Carbon Source Effect of Guangxi Typical Carbonate Rock Area by Acid Rain

2013· article· en· W2074055436 on OpenAlexaff
Shi Yu, Hui Yang

Bibliographic record

VenueApplied Mechanics and Materials · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsCanadian Association of General Surgeons
Fundersnot available
KeywordsCarbonateDolomiteWeatheringCarbonate rockGeologyCarbon fibersDissolutionMineralogyAcid rainGeochemistryMetallurgyMaterials scienceChemistry

Abstract

fetched live from OpenAlex

In order to have an insight into the reaction between acid rain and carbonate rock surface, and figure out the CO 2 carbonate source amount from the acid rain chemical weathering process of the carbonate rocks, two typical carbonate rock areas Guilin (represents limestone area) and Liuzhou (represents dolomite area) were chosen as the study areas in Guangxi. According to the dissolution rate calculated by the limestone test piece and GIS analysis, the CO 2 source produced by the acid rain was 41.066×10 8 g/a, in which Guilin was 33.349×108g/a and Liuzhou was 7.717×10 8 g/a. The carbon sources of unit area in Guilin and in Liuzhou were 66.967×10 5 g/a•km2 and 42.777×10 5 g/a•km2 respectively. Although the carbon sources were still less than their carbon sinks in Guilin and Liuzhou which were 273.891×105g/a•km2 and 43.660×105g/a•km2 respectively, they should not be neglected. There were two reasons that the degassing rate of carbon source in Guilin was slower than that in Liuzhou. One was the representative area of carbonate rock in Guilin were 2.77 times of that in Liuzhou, the other one was that the total intensity of acid rain of Guilin was lower than Liuzhou, so that the dissolution rate of the carbonate rocks was lower in Guilin.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.238
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 teacher head, not a consensus.

Study designBench or experimental
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

Citations2
Published2013
Admission routes1
Has abstractyes

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