Glioblastoma stem cells show transcriptionally correlated spatial organization
Bibliographic record
Abstract
DATASET_FINAL A. original_phase_contrast_images This compressed file contains all the images compressed as .mp4 videos containing images used in this study. samples : 15 GBM, IDH-wt GSCs Conditions : Grown in neural stem cell media with growth factors. Each video is a time-series of images from a single well. B. datasets_images_ilastik_training_and_manuscript_figures fig_original_images Contains the original uncropped images for figure 1 and supplementary fig 1. These are phase contrast images. A reference image with the original scale bar is provided. Ilastik_training_images_with_masks This folder contains the training images used for each sample to generate the masks for cellprofiler image analysis. Training and mask generation was carried out using ilastik. C. manuscript_analysis_data Files used in analysis Cellprofiler image feature output for each image GSVA scores for 111 gene signatures from 15 matched bulk gene expression sample DATASET_VALIDATION A. original_videos_validation This compressed file contains all the images compressed as .mp4 videos containing the validation images used in this study. samples : 4 GBM-derived GSCs Conditions : Grown in neural stem cell media with growth factors. Each video is a time-series of images from a single well. B. ilastik_masks_validation Ilastik_training_images_with_masks This folder contains the training images used for each sample to generate the masks for cellprofiler image analysis. Training and mask generation was carried out using ilastik.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.033 |
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".