Pohádky pro zlobivé strašidýlko
Bibliographic record
Abstract
A koho že strašidýlko zlobí? No maminku Bílou paní a tatínka Bezhlavého rytíře, se kterými bydlí na starém opuštěném hradě. A je tak neposedné, že maminka s ním má spoustu starostí a trápení. Strašidýlko si celý den jen poletuje po lesích, sklepeních nebo kolem cimbuří a večer celé rozdováděné nechce usnout. Maminka proto požádá starého netopýra Alberta, aby strašidýlku vyprávěl krátké pohádky na dobrou noc. A tak se malý strašidlácký kluk před spaním dozví, jak to bylo s tlustou vílou, co za trampoty měl ohniváček, kterak dopadl mlsný hejkal, proč křepelky volají pět peněz a mnoho dalších příběhů. A spolu s ním se to mohou dozvědět i vaše děti.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".