Supplementary Dataset for "Representativeness of Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux Sites"
Bibliographic record
Abstract
This record is for the dataset “Supplementary Dataset for "Representativeness of Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux Sites"” at https://doi.org/10.5281/zenodo.4015350 These datasets are supplementary to the paper "Representativeness of Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux Sites" by Chu et al. Dataset S1. Summary of site-specific footprint metrics filename: All_site_fpt_summary.csv readme: All_site_fpt_summary-README.csv Dataset S2. All monthly footprint climatology weight maps filename: monthly_footprint_climatology_weight_map.zip the zip folder contains individual files of all monthly footprint weight maps filename: _ _ _ _fpt_weight.tif readme: README.txt Dataset S3. All site-year footprint climatology overlapped with true-color satellite images. filename: site-year_footprint_climatology_realcolor_map.zip the zip folder contains individual files of footprint climatologies from all site-years filename: _ _ _shrink_footprint_climatology.png readme: README.txt Dataset S4. Site-specific results and representativeness index based on the land cover type analysis. filename: All_site_land_cover_dominant_summary2.csv readme:All_site_land_cover_dominant_summary2-README.csv Dataset S5. Site-specific results and representativeness index based on the EVI analysis. filename: All_site_Landsat_EVI_fpt_comparison2.csv readme: All_site_Landsat_EVI_fpt_comparison2-README.csv Dataset S6. All available site-month EVI and time-explicit representativeness. filename: All_site_Landsat_EVI_all_cutout2.csv readme: All_site_Landsat_EVI_all_cutout2-README.csv This dataset can be downloaded at https://doi.org/10.5281/zenodo.4015350
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.332 | 0.186 |
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".