Metadata för en systematisk beskrivning av signaldata
Bibliographic record
Abstract
Detta kapitel syftar till att ge en grundlig inblick i de olika typer av metadata som kan vara användbara vid design, optimering och reglering av reningsverk. Metadata kan grupperas i tre huvudkategorier: (a) metadata som beskriver hur signalen genererats, (b) metadata som beskriver signalens kvalitet och (c) kontextuell information om signalen och dess tillämpning. Var och en av dessa kategorier introduceras och förklaras i tre separata avsnitt. Detta kapitel ger svar på vad som anses vara metadata. I mindre utsträckning ges rekommendationer om urvalet av metadata för långtidslagring. I Kapitel 4 förklaras istället var och hur metadata bör lagras. Kapitel 5 och 6 förklarar hur metadata kan samlas in genom särskilda metoder för givarvalidering (Kapitel 5) eller med hjälp av 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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.048 |
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".