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Record W4308636446 · doi:10.1039/d2em00368f

An experimental and modeling study on the penetration of spilled oil into thawing frozen soil

2022· article· en· W4308636446 on OpenAlexafffund
Zhaonian Qu, Chunjiang An, Zhu Mei, Rengyu Yue, Shan Zhao, Feng Qi, Mengfan Cai, Jiyuan Wen

Bibliographic record

VenueEnvironmental Science Processes & Impacts · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPenetration (warfare)Environmental sciencePenetration rateContaminationPollutionOil spillPetroleum engineeringEnvironmental engineeringGeologyEcologyEngineering

Abstract

fetched live from OpenAlex

Oil spills are significant environmental accidents that have significant impacts on environmental and ecological health. Spill pollution in the cold regions may pose a particular challenge. To achieve a fast response, the oil transport mode such as penetration should be well understood. In this study, the oil penetration behavior in thawing frozen soil at different temperatures and water contents were investigated. The results showed the penetration behavior of spilled oil in the thawing frozen soil and the influence of salinity level. The modified Green-Ampt model could simulate the penetration process well especially with high water content, relatively cold temperature, and slow thawing rate. This study reveals the new features of oil penetration behavior and distribution patterns in thawing frozen soil under different conditions. Hence, it is of significant importance to support the rapid response measures and reduce the contamination of oil spill accidents in cold regions.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 communication0.0000.001
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 head0.039
GPT teacher head0.270
Teacher spread0.231 · 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 designSimulation or modeling
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

Citations9
Published2022
Admission routes2
Has abstractyes

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