Bibliographic record
Abstract
Abstract On early Christmas morning 1974, the city of Darwin in Australia’s Northern Territory was razed by one of the strongest tropical cyclones ever recorded on that continent. The storm killed 65 people and injured another 790 while destroying more than 80 percent of Darwin’s buildings and uprooting every tree in the city. A storm surge of 12 ft inundated the coast and capsized 21 vessels. Tracy caused at least $800 million in damages, making it the most costly tropical cyclone in Australia’s history. Situated in far northwestern Australia, Darwin is highly susceptible to strong cyclones. In 1875 the ship Gothenburg, en route from Darwin to Australia’s east coast, was sunk by a cyclone, killing 102, almost a quarter of Darwin’s fledgling population. Destructive storms struck the city again in 1878, 1881, 1897, 1917, and 1937. (Adding to its misery, Darwin was bombed about 60 times during World War II. The city’s general misfortune was attributed by the region’s Larrakia Aboriginal people to their pagan god Nungalinya, who became enraged by white settlers.)
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.102 | 0.020 |
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".