Bibliographic record
Abstract
Main changes pep8 parsing with pylint and general cleanup of the code. Volume(np_array) does not need array to be transposed anymore. Warning: this may cause flipping of the x-z axes! applications.py added SplinePlotter object pointcloud.py bug fix in clone() added labels2D() added logscale for colormapping in method cmap(..., logscale=True) added explicit label formatting for scalarbars, @XushanLu plotter.py added breakInteraction() to break window interaction and return to the python execution flow picture.py added class MatplotlibPicture to embed matplotlib figures as 2d objects in the scene. shapes.py Added text() method to update 3d text on the fly, by @mkerrin Added pcaEllipse() analogous to pcaEllipsoid() for 2D problems. utils.py added getUV() method to get the texture coordinates of a 3D point volume.py Volume(np_array) does not need array to be transposed anymore. Warning: this may cause flipping of the x-z axes! added slicePlane(autocrop=False) Examples New/Revised examples/basic/input_box.py examples/basic/pca_ellipse.py examples/advanced/capping_mesh.py examples/volumetric/numpy2volume2.py examples/volumetric/numpy2volume1.py examples/pyplot/fourier_epicycles.py examples/pyplot/histo_pca.py examples/pyplot/histo_1d_b.py examples/other/flag_labels.py examples/other/remesh_tetgen.py examples/other/pymeshlab2.py
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.348 | 0.456 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".