Bibliographic record
Abstract
A critical incident is defined as one of the most serious situations that\ncan occur at any institution. Among these critical incidents are fire\nhazards, which resulted in 224 civilian deaths, and approximately 1.5$\nbillion dollars in direct property damage in Canada in 2011 alone. As\na result of these costs, much effort is currently being placed in both\nfire safety prevention and fire safety evacuation. However, many current\ntraining techniques come at a high cost, and remain ineffective for\nreal life fire situations, especially those which take place in large institutions.\nAn example of this can be seen in university chemical labs, which\nhold many hazardous and flammable materials. In this thesis, the development\nof an incidence response training game for chemical lab fires\nis described. Serious games leverage the power of computer games to\ncaptivate and engage players/learners for a specific purpose such as to\ndevelop new knowledge or skills. Not only do serious games allow for\nhigher engagement rates, and easier distribution than current training\ntechniques, but by utilizing stereoscopic 3D (S3D) technologies it is\nhypothesized by many researchers that even higher engagement rates\nas well as knowledge retention levels can be reached. However, there is\na lack of research examining the effects of S3D within an incidence response\nserious game. In this thesis, three experiments were conducted\nto examine potential benefits of employing S3D within a serious game\nfor incidence response: i) engagement effects with S3D, ii) calibration\nof S3D settings, and iii) knowledge retention levels with S3D.\nThe results of these experiments revealed that users were neither\nmore engaged, nor did they increase their knowledge retention with the\nincorporation of S3D, contrary to prior research. Furthermore, allowing\na user to define their own S3D settings is critical, and the inability to\ndo so may create visual discomfort or an unnoticeable S3D effect.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".