MétaCan
Menu
← Back to cohort
Record W4293787161 · doi:10.21203/rs.3.rs-1984942/v1

Transfer and transformation of mercury in cement production

2022· preprint· en· W4293787161 on OpenAlexaboutno aff
Chongrui Yuan, Yifan Wang, Jie Yang, Shu‐Qing Yang, Junlin Fu, Shengyu Liu

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersChengdu UniversityEducation Department of Sichuan ProvinceChengdu Science and Technology BureauDepartment of Science and Technology of Sichuan ProvinceNational Natural Science Foundation of ChinaChengdu University of Information Technology
KeywordsMercury (programming language)CementEnvironmental sciencePollutionProcess engineeringComputer scienceEngineeringMaterials scienceMetallurgyEcology

Abstract

fetched live from OpenAlex

Abstract In the previous study, the Ontario method was used to sample and analyze some production links of cement plants, but only the morphological distribution of mercury before and after the dust collector was obtained. Few researches have been carried out to analyse and test the whole process of cement plant production. In this study, based on the method of combining theoretical analysis with field sampling, the mercury levels in solid samples and gas samples was analyzed and then compared with the results obtained from theoretical analysis. In this work, the results of the theoretical analysis and the sampling can be derived towards a consistent trend, so the theoretical analysis results have a certain credibility. Then the theoretical analysis of each link in the cement production process was carried out to obtain the morphological distribution of mercury in the main link, and then the thermodynamic analysis was carried out to obtain the form distribution, migration and transformation of mercury in each link of cement production. It provides a theoretical basis for local mercury pollution control, later mercury emission reduction, coordinated disposal and management.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.077
GPT teacher head0.381
Teacher spread0.304 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueResearch Square→Same topicMercury impact and mitigation studies→French-language works237,207→