SC-Earth: A Station-based Serially Complete Earth Dataset from 1950 to 2019
Bibliographic record
Abstract
Meteorological data from ground stations suffer from temporal discontinuities caused by missing values and short measurement periods. Gap filling and reconstruction techniques have proven to be effective in producing serially complete station datasets that are used for a myriad of meteorological applications (e.g., developing gridded meteorological datasets and validating models). We developed the serially complete Earth (SC-Earth) dataset, which provides global station-based daily precipitation, mean temperature, temperature range, dew-point temperature, and wind speed data from 1950 to 2019. SC-Earth utilizes raw station data from the Global Historical Climatology Network-Daily (GHCN-D) and the Global Surface Summary of the Day (GSOD). The five variables are precipitation (prcp), mean daily temperature (tmean), daily temperature range (trange), dew-point temperature (tdew), and wind speed (wind). Daily minimum and maximum temperature can be inferred from tmean and trange. Humidity variables can be inferred from tdew. There are three files for each variable. "observation" contains quality controlled raw station observations. "estimate" contains SC-Earth estimates for all days (including days that "observation" has values) by merging estimates from 15 strategies (quantile mapping, interpolation, machine learning, and multiple-strategy merging). "final" is the final SC-Earth output, which uses "estimate" to fill the gap that "observation" is not available. Reference: Guoqiang Tang, Martyn P. Clark, Simon Michael Papalexiou. (2021). SC-Earth: A Station-based Serially Complete Earth Dataset from 1950 to 2019. Journal of Climate.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".