MétaCan
Menu
Back to cohort

An Excel Macro for Generating Trilinear Plots

2006· article· en· W2113494925 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGround Water · 2006
Typearticle
Languageen
FieldDecision Sciences
TopicMultidisciplinary Science and Engineering Research
Canadian institutionsFuture Earth
Fundersnot available
KeywordsWorksheetMacroVisual Basic for ApplicationsMicrosoft excelComputer scienceMs excelComputer graphics (images)Plot (graphics)Set (abstract data type)GraphicsVisual BasicSimple (philosophy)Scatter plotEngineering drawingProgramming languageSoftwareSoftware engineeringMathematicsOperating systemStatisticsEngineering

Abstract

fetched live from OpenAlex

This computer note describes a method for creating trilinear plots in Microsoft Excel. Macros have been created in MS Excel's internal language: Visual Basic for Applications (VBA). A simple form has been set up to allow the user to input data from an Excel worksheet. The VBA macro is used to convert the triangular data (which consist of three columns of percentage data) into X-Y data. The macro then generates the axes, labels, and grid for the trilinear plot. The X-Y data are plotted as scatter data in Excel. By providing this macro in Excel, users can create trilinear plots in a quick, inexpensive manner.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.771
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.127
GPT teacher head0.421
Teacher spread0.293 · 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