Bibliographic record
Abstract
Data som produceras på reningsverk erhålls genom en mängd olika enheter, inklusive ställdon, styrenheter och sensorer. Detta resulterar i data som kan vara mycket varierande i sin struktur. Att hantera denna blandning och heterogeniteten i dataformat kan vara en utmaning när data ska lagras eller tolkas. Av denna anledning beskriver detta kapitel de strukturella aspekterna av data på ett vanligt reningsverk. Syftet med detta kapitel är att: Introducera grundläggande begrepp för att beskriva, förstå och hantera data som produceras av onlineinstrument såsom givare och ställdon (t.ex. ventiler och pumpar).Definiera de vanligaste termerna som används i rapporten och som rör givare och andra datakällor.Illustrerar definitionerna med praktiska exempel. Där så är möjligt har vi nyttjat befintliga standarder och referenser för definitionerna även om flertalet definitioner har tagits fram specifikt för denna rapport.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.063 | 0.071 |
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".