Bibliographic record
Abstract
The coronavirus which appeared in December 2019 in Wuhan has spread out\nworldwide and caused the death of more than 280,000 people (as of May, 11\n2020). Since February 2020, doubts were raised about the numbers of confirmed\ncases and deaths reported by the Chinese government. In this paper, we examine\ndata available from China at the city and provincial levels and we compare them\nwith Canadian provincial data, US state data and French regional data. We\nconsider cumulative and daily numbers of confirmed cases and deaths and examine\nthese numbers through the lens of their first two digits and in particular we\nmeasure departures of these first two digits to the Newcomb-Benford\ndistribution, often used to detect frauds. Our finding is that there is no\nevidence that cumulative and daily numbers of confirmed cases and deaths for\nall these countries have different first or second digit distributions. We also\nshow that the Newcomb-Benford distribution cannot be rejected for these data.\n
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".