SCDNA: a serially complete precipitation and temperature dataset in North America from 1979 to 2018 (Version 1.1)
Bibliographic record
Abstract
Version updates: Version 1.1 is generally consistent with Version 1 in data estimation but (1) provides source flags in the final dataset, (2) adds individual station files for every merged station, and (3) excludes some stations due to their quality problems. Station-based serially complete datasets (SCDs) of precipitation and temperature observations are important for hydrometeorological studies. We developed a SCD for North America (SCDNA) of precipitation, minimum temperature, and maximum temperature from 1979 to 2018. Raw meteorological station data were obtained from the Global Historical Climate Network Daily (GHCN-D), the Global Surface Summary of the Day (GSOD), Environment and Climate Change Canada (ECCC), and a compiled station database in Mexico (Livneh et al. 2015). There are three types of missing values that are infilled/reconstructed by this dataset: Missing value during the observation period when the station still works. Missing value beyond the observation period (reconstruction period) before the station is deployed or after the station ceases working. Station measurements that fail quality control checks are treated as missing values and imputed. This dataset is useful for various purposes of applications that require: Quality-controlled actual station observations from multiple datasets in North America; Station observations without missing values in the observation period; Serially complete station observations. Users should be cautious when using this dataset for trend analysis because it is possible that trends are not well reconstructed. Three types of dataset files are provided: “SCDNA_v1.1.nc4”. This NetCDF file contains basic information (ID, location, elevation) and the final variables of stations. For each variable (precipitation, minimum temperature, and maximum temperature), this file provides the serially complete data, the estimation flag indicating whether a value is from observation or estimation, and accuracy index (KGE) of estimated data. “SCD_complete_part1.zip” to “SCD_complete_part10.zip”. These ten compressed files contain complete data for the production of the SCD, including quality flags, estimates from 16 strategies (quantile mapping, interpolation, machine learning, and multiple-strategy merging), corrected/uncorrected SCD estimates, accuracy indices, etc. "overlap_station.zip". This file contains the list and data of stations that have the same latitude and longitude records due to various reasons, such as the same stations from different sources, naming rules, recording bias, etc, network design, etc. We recommend that users download "SCDNA_v1.1.nc4" for quick and direct application, and adopt the second type for in-depth investigation of different strategies and potential methodology improvement. Please refer to Readme.txt for more details. The codes used to produce this dataset are available on GitHub (https://github.com/tgq14/GapFill).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".