The quot Flu Seasons quot and the Missing Data A Matched Pair Analysis Northern and Southern Hemispheres and Hong Kong China
Bibliographic record
Abstract
Background The matched pair analysis does not compare between viruses the magnitude of positivity rates or the number of positive specimens from different regions Each virus among n is match paired with itself in the two responses of its own partial table The Cochran Mantel Haenszel Test collapses all these partial tables with all the n observations in a x x n contingency table to yield the marginal counts of the McNemar rsquo s Test We want to know if when and how it holds for our live presentations of the Laboratories rsquo real time observations nbsp Methods and Results We used col wk for North America Europe Asia and South America Africa Australia New Zealand For Canada Row Row was BC Manitoba Ontario Atlantic For the US Row Row was Regions In addition we performed simultaneous Proportional Odds Comparison of Margins x Table And we sequentially deleted Regions and to define the effects of the missing data And we surveyed for ILI pneumonias in Hong Kong for matched pair regression A H and A H surged resurged with condition numbers multi co linearity lt eigen value max eigen value min max min At above the regression coefficients diverged in opposite directions Conclusion We define the Influenza Season mathematically with the McNemar rsquo s Test using the Laboratories rsquo real time observations from the Americas Europe Asia Africa and Australia New Zealand These real time sequential frames from the weekly updated data show that Z n n n n MathType MTEF feaaguart ev aaatCvAUfeBSjuyZL yd gzLbvyNv CaerbuLwBLnhiov DGi BTfMBaeXatLxBI gBaerbd wDYLwzYbItLDharqqtubsr rNCHbGeaGak Jf crFfpeea xh v qiW rqqrFfpeea xe Lq Jc vqaqpepm xbba pwe Q fs yqaqpepae pg FirpepeKkFr xfr xfr xb adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamOwaiabg da maaliaabaWaaeWaaeaadaqfqaqabSqaaiaaigdacaaIYaaabeqdbaGaamOBaaaakiabgkHiTmaavababeWcbaGaaGOmaiaaigdaaeqaneaacaWGUbaaaaGccaGLOaGaayzkaaaabaWaaeWaaeaadaqfqaqabSqaaiaaigdacaaIYaaabeqdbaGaamOBaaaakiabgUcaRmaavababeWcbaGaaGOmaiaaigdaaeqaneaacaWGUbaaaaGccaGLOaGaayzkaaaaaiabgEIizlaaicdacaGGUaGaaGynaaaa D D holds both for the normal and for the approximate standardized test statistics We report live how this matched pair model performs with the values of the interim missing data set to be zero as these were the interim observations
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".