Data and Analysis Code for Long-Term Trend and Correlation Analysis of Daymet Shortwave Radiation, CERES Solar Insolation, ERA5 Total Cloud Cover, MODIS Cloud Fraction, In-situ Cloud Cover Observations, and Landsat Lake Surface Water Temperature in Northwest Territories (NWT), Canada (1980–2023)
Bibliographic record
Abstract
The dataset presented here comprises processed data and accompanying code for preprocessing, processing, and analyzing multiple climate-related datasets used in the study "Trend analysis of surface shortwave radiation in continental Northwest Territories, Canada (1980–2023)." Organized into several directories—technical_validation, ceres_solar_insolation, correlation, daymet_ssr, era5_tcc, modis_cldfr, summer_ssr_cloud_anomaly, and summer_ssr_lswt_mktrend—this submission includes output CSV files that detail correlations between Daymet shortwave radiation (SSR), CERES solar insolation, ERA5 total cloud cover (TCC), MODIS Cloud Fraction, in-situ cloud cover observations, and Landsat-derived lake surface water temperature (LSWT). Each directory contains the necessary scripts for data preprocessing and analysis, ensuring reproducibility and facilitating further research. Due to the substantial volume of raw data, only the processed output files and associated code are provided. Additionally, a readme.txt file is included to guide users through the repository structure and the utilization of the provided resources effectively.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.130 | 0.098 |
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".