Bibliographic record
Abstract
The hundredth anniversary of the worst ever civilian maritime disaster was also the fiftieth anniversary of the death of arguably its most controversial character, Captain Stanley Lord, skipper of the Californian, a "tramp" steamer that became entrapped in ice just off the Grand Banks of Newfoundland on April 14, 1912. Although Lord was faulted in two widely publicized inquiries for failing to respond to Titanic's distress signals, there may have actually been a medical reason for his behavior because he suffered from chronic renal disease and most likely had some secondary cognitive impairment due to this disease. An assessment of Lord's health history shows that he fractured his leg as a young man; suffered from poor eyesight, which led to his premature retirement from the sea by the age of 50; and eventually died from renal failure. Furthermore, his death certificate alludes to previous uremic episodes, perhaps encompassing the time period of the Titanic accident. Lord may have been under some pressure not to reveal his infirmity because doing so could have further jeopardized his career. The literature abounds with evidence that renal insufficiency negatively affects cognition, often years before progression to end-stage renal disease. Captain Lord's failure to act in a crisis situation may serve as a case in point.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".