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Record W6950562764 · doi:10.5281/zenodo.7656958

XMapTools 4.1

2023· other· en· W6950562764 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsWestern University
FundersHorizon 2020 Framework Programme
KeywordsVisualizationSoftwareData visualizationVariety (cybernetics)Calibration

Abstract

fetched live from OpenAlex

Release of XMapTools 4.1 (build 230102). XMapTools is available as standalone applications compiled for macOS (Intel / Apple Silicon) or WINDOWS. XMapTools is an advanced analysis software for quantitative chemical analysis of solid Earth materials in 1D, 2D and 3D. It provides numerical tools and packages implemented in a guided and versatile environment allowing data to be explored and visualised in your own way. XMapTools incorporates for example a large variety of options for data processing including routines for classification, segmentation, calibration and visualisation via single and multi-channel maps or via binary, ternary and spider diagrams.

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)0.005
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: Software
Teacher disagreement score0.230
Threshold uncertainty score0.770

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0020.000
Scholarly communication0.0050.004
Open science0.0050.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.2300.244

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 head0.048
GPT teacher head0.263
Teacher spread0.215 · 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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)French-language works237,207