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Record W2164723612 · doi:10.5589/m10-070

Quick Profiler (QuiP): a friendly tool to extract roughness statistical parameters using a needle profiler

2010· article· en· W2164723612 on OpenAlexfundvenueno aff
Mélanie Trudel, François Charbonneau, Fernando Carlos Avendaño, Robert Leconte

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsnot available
FundersAgriculture and Agri-Food Canada
KeywordsGraphical user interfaceWind profilerRemote sensingComputer scienceRadarInterface (matter)Surface roughnessEngineeringEngineering drawingGeographyTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

QuiP is a graphical user interface (GUI) developed in MATLAB to extract and analyze soil surface profile measurements obtained from a needle profiler in a few minutes. Surface roughness parameters can be extracted from any needle profiler with simple modifications to the tool. QuiP calculates many statistics useful in radar remote sensing while generating the profiles. Furthermore, a QuiP function can be used to join several profiles. Long profiles can therefore be created from a short profiler (e.g., 1 m) that is easier to carry and handle in the field. Extraction of profiles by QuiP is repeatable and therefore reduces user’s errors from data processing.

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)0.004
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.065
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0650.019

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 head0.015
GPT teacher head0.245
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 designBench or experimental
Domainnot available
GenreMethods

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

Citations20
Published2010
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Remote SensingSame topicSoil Moisture and Remote SensingFrench-language works237,207