Preparation national team of Kuvait on Asiatic winter games and analysis of game performance during the tournament.
Bibliographic record
Abstract
Title: Preparation ofKuwait national team on Asian winter games and iťs functioning in tournament. Goal of my dissertation work is to capture methodic and tactics preparation of Kuwait national team for Asian winter games, which took place during 26th ofJanuary and 3th ofFebruary 2007. It went together with preparation and also with team functioning in tournament. In my dissertation work I capture realization of hockey national team's preparation when I use typical training units on ice to reach ideal training cycle with the feedback from Asian winter game organization. Purpose on Work: Purpose on my work is to put together plan for Kuwait national team Asian winter games. After 1994 Czech hockey become authority notjust between hockey worlds known countries but also in some exotic countries. This fact helped in my selection for post ofKuwait national hockey team Head coach. In 1999 Kuwait national team was headed by Canadian couch, which after his functioning announced news: " How come is possible to play hockey here ifthey built hockey arena on dessert". As a FTVS student I took this engagement as a challenge that even young trainers can carry- through abroad. Also it shows knowledge ofFTVS student and its use in work experience. Method: Dissertation work search exposure ofpreparation time on player...
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".