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Record W6950616730 · doi:10.5281/zenodo.8397416

MOIST Source code

2023· other· en· W6950616730 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSource codeExecutableRowVariable (mathematics)Code (set theory)MATLABFunction (biology)Matrix (chemical analysis)

Abstract

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Source code of MOIST for theoretical, semi-analytical, long-term field tests. A semi-coupled version of MOIST is also included, which is used for exploring the advantage of fully-coupled version on various spatial and temporal discretization. [Long term validation data can also be download via https://www.pc-progress.com/en/Default.aspx?h1d-lib-isotope]. HOW TO RUN: Each '.rar' has a 'Main_program' or 'Main_program_2' file. It is executable when all the function files are located under the same path. These codes were developed by MATLAB R2022a, thus, we recommend to use R2022a or later, although our tests showed that R2019a also works. A step-by-step explanation on how to run source code can be found from "version 1.0 readme" on the right hand side of this page. OUTPUT VARIABLES There are plenty of output variables after the calculation is done. Following variables could be considered the most important. Each variable is a matrix with N rows and D columns, where N is the soil layers and D is the number of days. z_theta output of soil water content profiles z_cil output of isotope profiles ('Delta_seg1' in semi-coupled version) z_T output of temperature profiles z_h output of soil water head profiles Besides, in the semi-analytical test: zBA results of isotope profiles under non-isotherm and non-saturated conditions zBA1 results of isotope profiles under isotherm and saturated conditions Furthermore, in theoretical tests, pTest is used to select test 1-6. However, it is fixed at 6 in other tests. Note that the some functions are written in C and Matlab, and error may occur if relavent ".dll" files are unable to be found in local mechine. (Please refer to https://www.mathworks.com/help/matlab/matlab_external/invalid-mex-file-error.html) We tested these codes before uploading under the windows platform and everything works well. However, if you found codes cannot run on your end, welcome to report error details on https://github.com/HAN-2/MOIST_ERRORs/issues or send an email to haf033@usask.ca (Han). I will try my best to debug it and thank you for your interest!

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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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.295
Threshold uncertainty score0.987

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0050.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.2950.227

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 head0.043
GPT teacher head0.223
Teacher spread0.181 · 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 designNot applicable
Domainnot available
GenreSoftware

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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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