Bibliographic record
Abstract
Geostatistics analyzes and predicts the values associated with spatial or spatio-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This update to the Zenodo archive (version 3) consists of two archives consisting of geostatistical analyses of SARS-CoV-2 testing data: Archive #1: "1. Updated Geostatistical Analysis of SARS-CoV-2 in the United States (version 3 - July 17th 2020).zip" - a new set of results from geostatistical analyses performed against corrected COVID-19 case data (according to source-based errata documents) for the continental United States, spanning from March 24th through to July 13th 2020. Archive #2: "2. Initial Geostatistical Analysis of SARS-CoV-2 in the United States (version 2 - first published June 15th, 2020).zip" - the results from the geostatistical analyses performed for version 2 of this Zenodo archive which analyzed COVID-19 case data prior to any case correction step. These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*), Cluster and Outlier analysis (Anselin Local Moran's I), and Spatial Autocorrelation (Global Moran's I). Note that the older archive also provides analytics that are not yet available in version 3, but will be incorporated in future releases of this archive (e.g. spatial-temporal analyses, and point-to-point comparisons with Kriging and Densification analysis). The Word document ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 3 - published July 17th 2020).docx") describes in detail the contents of each file and folder within these two archives.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".