Sampson flat community bushfire experiences
Bibliographic record
Abstract
After a major bushfire impacted communities less than two kilometres from Adelaide’s suburban edge in January 2015, the South Australian Country Fire Service (CFS) commissioned this research to explore three\nkey questions: what factors affected residents’ planning, preparation and actions on the day; the influence of CFS Community Fire Safe groups on bushfire safety; and the effectiveness of information and warnings for people living in the rural/urban interface. The approach followed studies conducted after other major bushfires. The findings were similar, however they provided some new insights, including that although\nthe majority of people felt physically prepared for a fire, only half felt emotionally prepared for the impacts of the fire and its aftermath. Just over one quarter of respondents had a written bushfire survival plan (a strong result compared with the average seen in previous studies of five percent) and nearly 90% had had a discussion about what to do in the event of a bushfire. Being part of a Community Fire Safe group had a positive impact on both planning and preparation. This project showed that the collective learnings from other post-fire studies and the actions being taken by CFS to implement these learnings are being translated\ninto actions in the community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".