Additional file 3 of Deficits in coordinated neuronal activity and network topology are striatal hallmarks in Huntington’s disease
Bibliographic record
Abstract
Additional file 3: Figure S3. Litter-grouped spontaneously active neurons and coordinated ensemble activity. The data is the same as Fig. 1 of the main text but, to evaluate intra- and inter-litter variability, the measurements are separated according to litters and for both WT and HD. The plotted data correspond to: (A) percentage of active neurons in the cultures (STR WT n =3, 4, 13, 14, 4; STR HD n =3, 3, 12, 12, 4; CTX WT n =2, 2, 6, 3, 3, 2; CTX HD n =3, 2, 6, 6, 2, 3) (B) percentage of active neurons that participate in spontaneous network burst (STR WT n =1, 0, 13, 7, 0; STR HD n =1, 1, 9, 5, 0; CTX WT n =3, 2, 6, 3, 3, 2; CTX HD n =3, 2, 6, 6, 1, 3), (C) average network inter-burst interval (IBI) (STR WT n =1, 0, 13, 8, 0; STR HD n =1, 0, 9, 5, 0; CTX WT n =3, 2, 6, 3, 2, 1; CTX HD n =3, 2, 6, 5, 2, 3), (D) global activity rate of the cultures (STR WT n =3, 4, 13, 14, 4; STR HD n =3, 3, 12, 12, 4; CTX WT n =3, 2, 5, 3, 3, 2; CTX HD n =3, 1, 6, 6, 1, 3) and (E) fraction of independent spikes (STR WT n =3, 4, 12, 13, 4; STR HD n =3, 3, 12, 14, 4; CTX WT n =3, 2, 6, 3, 1, 2; CTX HD n =3, 2, 6, 6, 2, 2). Each dot in the plot represents a single culture, and each column a different litter, thick line the mean, thick shaded area the standard error of the mean and thin shaded area the standard deviation.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.892 | 0.166 |
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".