Strukturerad lagring av signaler samt deras metadata
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
Detta kapitel syftar till att ge en översikt över hur signaldata och deras metadata kan struktureras, med betoning hantering och lagring. Specifikt fokuserar det på: (a) nyttan för organisation, (b) vilka data som ska lagras och vad som ska behållas, och (c) datahanteringsmetoder. I detta kapitel ges alltså svar på var och hur metadata ska lagras på ett effektivt sätt. I Kapitel 3 förklarades vad som anses vara metadata. I Kapitlen 5 och 6 beskrivs hur man samlar in vissa metadata genom särskilda valideringstester av sensorer (Kapitel 5) eller med dataanalytiska metoder (Kapitel 6).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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