Bibliographic record
Abstract
Risk management is all about being proactive. Risk analysis is undertaken to identify where things might go wrong. This does require some experience and, indeed, the wider your experience the more likely you are to be able to recognize issues as problems or to identify potential problems. The general principles of risk management are presented quite lucidly in the Australia/New Zealand Standard AS/NZS 4360:1999 (Standards Australia, 1999) and will be discussed later in this Chapter. “Why bother with that? It's never happened here!” How often have you identified a potential problem, only to be told “Oh that's never been a problem here,” or “We've never had a problem with that,” or “Why waste our time, that's just so unlikely”? Of course, the truth is that this head-in-the-sand mentality is exactly why some of the worst problems in IVF labs have arisen. We have personally experienced situations where an identified risk was pooh-poohed by the Medical Director, General Manager or equivalent, only to have just that problem occur a few weeks later – although professional confidentiality clearly precludes quoting specific examples! The dreaded “It's never happened here …” should probably be considered a warning bell that a proper risk assessment should be undertaken forthwith. After all, Captain Edward John Smith hadn't hit any icebergs before the maiden voyage of the Titanic, either!
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".