MétaCan
Menu
Back to cohort
Record W2792358998 · doi:10.1080/19386362.2018.1439671

Performance evaluation of TBM clogging potential for plain and conditioning soil using a newly developed laboratory apparatus

2018· article· en· W2792358998 on OpenAlexafffund
Chao Kang, Yaolin Yi, Alireza Bayat

Bibliographic record

VenueInternational Journal of Geotechnical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsCloggingGeotechnical engineeringPenetration (warfare)Soil waterEnvironmental scienceRotational speedPetroleum engineeringEngineeringSoil scienceMechanical engineering

Abstract

fetched live from OpenAlex

Researchers have proposed different methods to assess the clogging potential of soil, which mainly include an analytical method and semi-empirical method. However, most of them do not consider several main dominating factors, including penetration speed and rotational velocity. Therefore, a new apparatus was developed that can simulate the drilling process with penetration speed, penetration depth and rotational speed under control. The authors sampled and tested two different clayed soils, and the results agreed with field observations. Therefore, the performances of different additives were also evaluated according to the results from the new apparatus. The clogging test results of plain and conditioned soils were also compared with the results from the empirical diagram and plasticity index, showing that the new apparatus has the ability to assess the degree of clogging and differentiate the performance of additives.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.016
GPT teacher head0.264
Teacher spread0.249 · 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

Citations21
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Geotechnical EngineeringSame topicTunneling and Rock MechanicsFrench-language works237,207