Imaging how and where we breathe oxygen: another Big Short?
Bibliographic record
Abstract
The Big Short tells the story of a small group of skeptics who profited from the financial crisis in 2007 by betting against collateralized (mortgage) debt obligations (CDO). Importantly, the novel paints a clear picture of the eccentric nature of contrarians who think divergently and against the grain or bet against an accepted truth or “sure” thing. In a similar manner, Ishii and co-workers’ recent work describes their team’s development of a pulmonary imaging technology that provides divergent and disruptive in vivo lung measurements of oxygen partial pressure in the context of the prevailing and longstanding consensus around FEV1 as the definitive diagnostic of chronic lung disease. The Big Short tells the story of a small group of skeptics who profited from the financial crisis in 2007 by betting against collateralized (mortgage) debt obligations (CDO). Importantly, the novel paints a clear picture of the eccentric nature of contrarians who think divergently and against the grain or bet against an accepted truth or “sure” thing. In a similar manner, Ishii and co-workers’ recent work describes their team’s development of a pulmonary imaging technology that provides divergent and disruptive in vivo lung measurements of oxygen partial pressure in the context of the prevailing and longstanding consensus around FEV1 as the definitive diagnostic of chronic lung disease.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".