Additional file 1 of Fear response-based prediction for stress susceptibility to PTSD-like phenotypes
Bibliographic record
Abstract
Additional file 1: Supplemental Fig. 1. ITI freezing as a criterion for prediction of stress susceptibility. (A) A behavioral timeline. After an initial 120 s acclimation period, mice were subjected to 4 trials of tone CS. CS were co-terminated with a foot shock. Each CS lasted for 30 s, and was presented in pseudorandom order with a 90 s ITI (range 60–120 s). (B) Distributions of ITI freezing data from susceptible (red) and resilient (blue) mice. Distributions of freezing data during the 1st ITI (top left): resilient, μ = 2.1403, σ = 3.587; susceptible, μ = 13.3774, σ = 16.6243; green, 4.4324 where normalized Z1 = Z2. Distributions of freezing data during the 2nd ITI (top right): resilient, μ = 16.7149, σ = 18.3964; susceptible, = 35.7977, σ = 21.6703; green, 25.606, where normalized Z1 = Z2. Distributions of freezing data during the 3th ITI (bottom left): resilient, μ = 36.0558, σ = 24.4761; susceptible, μ = 50.4523, σ = 24.1190; green, 43.1805, where normalized Z1 = Z2. Distributions of freezing data during the 4th ITI (bottom right): resilient, μ = 26.6585, σ = 16.1910; susceptible, μ = 54.9368, σ = 22.3665; green, 38.6686, where normalized Z1 = Z2. (C) Criterion for categorization of mice into susceptible and resilient groups. Black line is the criterion that connects the green points in panels B. Plots show means ± SEMs.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.838 | 0.128 |
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".