A Socially Inspired Framework for Human State Inference Using Expert Opinion Integration
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.
Bibliographic record
Abstract
A complete biosensing involves two processes: data acquisition or collection and information inference. In this paper, a socially inspired framework to infer the human state using multiple cues or signals and inference techniques or “experts” is presented. A general idea with the proposed framework is that conventional inference algorithms are viewed as inference experts and then the inference problem can take advantage of the knowledge in expert opinion elicitation. The sense of the socially inspired lies in that 1) there are multiple cues, 2) there are multiple experts, 3) different experts have different expertise levels on different cues in association with different human states, and 4) there are different procedures to come up with a consensus or agreed opinion (i.e., human state in this case). To demonstrate the effectiveness of the proposed framework, inference of the fatigue state is taken as an example. The result is compared with that in a previous study in the literature and overall, it has been found that the proposed framework can deliver better results in terms of the inferring accuracy.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it