Bibliographic record
Abstract
Best Linear Unbiased EstimateAims: This paper presents estimates of the spread rate of Covid-19 in Canada, Mexico, and the USA from the early days of 2020 to October 9, 2021.Methods: Because it is impossible to measure and model all of the forces that can affect this spread rate, a statistical technique is used that produces a separate spread rate for each observation where differences in these estimates are due to omitted variables.Some of the most important omitted variables whose influence on the spread rate is captured in this paper's estimates include the imposition of social distancing laws, the degree that social distancing laws are observed, what percent of the population has been vaccinated and who was vaccinated, mutations of the virus, the density of the populations, and weather conditions.This paper's estimates are of the change in Covid-19 cases in time period t+1 due to an additional case in time period t [d(cases t+1)/d(cases t)] where t and t + 1 are one week apart.Results: I found that if the number of Covid-19 case can be reduced by one in time t then the number of cases in time t+1 fall by less than one; in contrast if the number of cases in time t rise by one, then the number of cases in time t+1 increases by more than one.Conclusion: it is harder to kill Covid-19 than it is for Covid-19 to spread.Thus governments and people should do all that they can to fight this 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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".