A Socially Inspired Framework for Human State Inference Using Expert Opinion Integration
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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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".