Poučevanje deskanja na snegu
Bibliographic record
Abstract
Atiju, mojemu prvemu učitelju bordanja.Hvala staršem in ožji družini, brez vas vse šolanje in lovljenje otroških sanj ne bi bilo mogoče.Hvala, da ste me že od malega vzpodbujali, mi omogočali najrazličnejša gibanja in študij ter mi priučili in privzgojili veselje do predajanja znanja.Posebej hvala mami, da ostajaš moja skala in me poganjaš naprej.Hvala tudi Rok, da si občasno moj bordarski trener in si mi s tem dodal nekaj mehkobe in norčavosti v gibanju.Hvala moji Urški, ki je tako potrpežljiva z menoj in me vzpodbujajoče spremlja na moji poti.Hvala, da si pomagala ustvariti to delo s svojimi fotografsko-snemalnimi sposobnostmi.Brez tebe ne bi zmogla.Hvala mentorju, prof.Mateju Majeriču, ki me je skozi študij vzpodbujal in mi dodal nekaj potrebne samozavesti.V letih študija je bil moj difovski vzornik.Hvala vsem sošolcem in vsem tistim, ki ste z mano švicali v gimnastični dvorani.Pokazali ste mi, da je včasih življenje treba jemati malo manj resno.Študijskih 'šol v naravi' ne bom pozabila nikoli. Ključne besede: šola deskanja na
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".