Bibliographic record
Abstract
GEOGRAFSKI HORIZONT -BROJ 2/2018Prije odlaska na samu olimpijadu, učenici su sudjelovali na pripremama na Geografskom odsjeku PMF-a Sveučilišta u Zagrebu, s ciljem produbljivanja znanja o određenim geografskim temama i stjecanja vještina potrebnih za međunarodno natjecanje.Tijekom priprema, održanih od 2. do 4. 7. 2018., učenicima su predavanja i radionice održali doc.dr.sc.Ivan Čanjevac, doc.dr.sc.Mladen Maradin, doc.dr.sc.Dubravka Spevec, doc.dr.sc.Ružica Vuk i dr.sc.Ivan Šulc.Učenici su tijekom priprema usvojili nova znanja i vještine iz područja klime i klimatskih promjena, prirodnih rizika povezanih s vodama, prirodnih resursa, turizma, kartografije te statističkih i grafičkih metoda u geografiji.Pod vodstvom team lidera, učenici su također izradili poster na temu Ston's "White Gold" from the Adriatic Sea.Naime, svi timovi su trebali pripremiti poster na temu Appreciating Landscapes, s naglaskom na utjecaj vode na ljudske djelatnosti.Međunarodno natjecanje održano je na kampusu Université Laval u gradu Quebécu, gdje su bili smješteni natjecatelji i voditelji.Natjecanje se sastojalo od ukupno tri dijela, ispiti su se održavali na Faculté de foresterie, de géographie et de géomatique.Dana 1. 8. učenici su pisali pismeni ispit (Written Response Test), 2. 8. su sudjelovali na terenskom radu (Field Work) u okolici grada Quebéca, 3. 8. su na fakultetu pisali ispit vezan uz terenski rad, a 5. 8. su rješavali multimedijski
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 0.011 |
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".