Characterization of Hard and Soft Sources of Information: a Practical Illustration
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
Abstract—Physical sensors (hard sources) and humans (soft sources) have complementary features in terms of perception, reasoning, memory. It is thus natural to combine their associated information for a wider coverage of the diversity of the available information and thus provide an enhanced situation awareness for the decision maker. While the fusion domain mainly considers (although not only) the processing and combination of informa-tion from hard sources, conciliating these two broad areas is gaining more and more interest in the domain of hard and soft fusion. In order to better understand the diversity and specificity of sources of information, we propose a functional model of a source of information, and a structured list of dimensions along which a source of information can be qualified. We illustrate some properties on a real data gathered from an experiment of light detection in a fog chamber involving both automatic and human detectors.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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