Human Systems Integration and Situation Awareness in Microworlds: An Examination of Emergency Response within the Offshore Command and Control Training System
Bibliographic record
Abstract
Existing guidelines detail assessment criteria that should be used to evaluate offshore emergency response (ER) team membersâ performance; however, minimal research has investigated this testing. Therefore, using a Human System Integration approach, this thesis examines the impact of including an electronic Emergency Response Focus Board (ERFB) during simulation testing. Archival ER performance videos were analyzed, subject matter experts (SMEs) were interviewed, and an iterative human-centered design process was used to test prototype ERFBs. Situation awareness, accuracy, and reaction times were collected during ERFB testing in simulated emergencies. Results indicate that SMEs use different assessment factors to predict future ER performance and that the type of ERFB and offshore experience significantly influenced speed and accuracy of responses. Based on these results, it was concluded that a dynamic ERFB improves the development and maintenance of SA. Therefore, it was recommended that a similar ERFB configuration be implemented into future offshore ER assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".