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Record W2061741415 · doi:10.2478/amsc-2014-0022

Application of GIS Methods in Assessing Effects of Mining Activity on Surface Infrastructure/Zastosowanie Metod Gis W Ocenie Wpływu Działalności Górniczej Na Infrastrukturę Na Powierzchni

2014· article· en· W2061741415 on OpenAlexaffabout
Jan Blachowski, Adam Chrzanowski, Anna Szostak-Chrzanowski

Bibliographic record

VenueArchives of Mining Sciences · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Mining Engineering
Canadian institutionsUniversity of New Brunswick
FundersPolitechnika Wrocławska
KeywordsDisplacement (psychology)Data miningSubsidenceCurvatureField (mathematics)Geographic information systemTilt (camera)Deformation (meteorology)Process (computing)Surface (topology)GeodesyGeographyComputer scienceGeologyMathematicsGeotechnical engineeringRemote sensingMining engineeringGeometryGeomorphologyMeteorology

Abstract

fetched live from OpenAlex

Abstract Tilt (T), curvature (K) and horizontal strain (ε) in ground subsidence troughs are the basic deformation parameters, which are used in the assessment of mining effects on surface infrastructure. The parameters can be determined from mathematical functions describing the continuous displacement field. The latter can be obtained by the least squares fitting of selected displacement functions to results of three-dimensional monitoring of horizontal and vertical displacements at discrete points. A methodology based on spatial data modelling in Geographic Information Systems (GIS) facilitates the above process as demonstrated on the example of a mining area in Canada. Polish guidelines regarding classification of mining risk categories based on the values of these parameters have been used in the example.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.005
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.280
Teacher spread0.270 · 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 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

Citations10
Published2014
Admission routes2
Has abstractyes

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