Bibliographic record
Abstract
Computer-aided chess is an important teaching method, as it allows a student to play under every condition possible, and regulates the speed of his/her development at an incremental pace, measured against actual players in the rated chess community.It is also relatively inexpensive, and pervasive, and allows players to match themselves against competitors from across the world.The learning process extends beyond games, as interactive software has shown; it teaches several skills, such as opening, strategy, tactics, and chess-problem solving.Furthermore, current applications allow chess players to establish rankings via online chess tournaments, meet international grandmasters, and have access to training tools based on strategies from chess masters.Using 250 chess software packages, this research classifies them into distinct categories based mainly on the Gobet and Jansen's organization of the chess knowledge [4].This is followed by extensive discussion that analyzes these training tools, in order to identify the best training techniques available building on a research on human computer interaction, cognjtive psychology, and chess theory.I will also always be grateful to my parents.Their unyielding support, academic guidance, and motivation have helped to shape both a positive opinion of myself, but also of my future and development.Due their influence, I am able to improve everyday, doing tomorrow what I thought impossible today.Finally, many thanks for my brother Basheer and my sister Salam, for always being pushy in the best possible way.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
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".