Bibliographic record
Abstract
Have you ever watched news coverage of an earthquake, a bus crash, or an explosion and had a sudden flash of guilt as you caught yourself thinking, "Boy, I'm glad that didn't happen near our emergency department"?Has this led to the more important thought, "What would we do if this happened near our overcrowded emergency department"?The issue of surge capacity is vital to emergency departments and not so foreign a concept as we monitor the patient acuity, available space, and equipment availability throughout the average shift.In the above mass casualty examples, there is a limited time to adapt to a sudden influx and perform the interventions shown to most affect the mortality rates.The metamorphosis from normal operations to disaster mode is referred to as surge capacity, which can be defined as "the ability to cope with increased numbers of casualties" (Chapman & Arbon, 2008, p. 6).Disaster surge capacity is much more onerous than the handling of normal daily surge and is sometimes broken into conventional, contingency, and crisis capacity depending on the extent to which a deviation from normal practices and standards is required (Altevogt, Stroud, Hanson, Hanfling, & Gostin, 2009, p. 52).
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 | 0.003 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 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".