Surface albedo impact on afforestation / reforestation carbon crediting projects
Bibliographic record
Abstract
Purpose and usage This dataset was created to analyse the impact of surface albedo change on afforestation/reforestation/revegetation carbon crediting projects. The python code used to perform the analysis is available on Github. Data description Specific data The two zip files 'project_geospatial_data.zip' and 'project_description_reports.zip' gather all the public documentation, extracted from Verra's registry, that were used in the study. The Excel file 'ARR_project_database.xlsx' contains all the specific data extracted to calculate the impact of surface albedo change. It presents the characteristics of each project as well as the treatment method to use the geospacial data files. See the tab #Read_me. The following CSV are the one used to perform the calculation of the albedo impact: proj_desc.csv : created from the tab #Desc_data in ARR_project_database. geodata_layers.csv : created from the tab #Geospacial_data in ARR_project_database. land_cover_type.csv : created from the tab #Land_cover_type in ARR_project_database. albedo_proxy_table.csv : created from the analysis of the surface albedo dataset. A land cover type can be approximate by another land cover type if the absolute difference between both is less 0.03 for a same location. This table provides the results for a global scale and for the land cover type consistent with the study. Global data The surface albedo dataset is under embargo for upcoming publication [E-mail contact adress : kathryn.loog@polymtl.ca]. The radiative kernel dataset for albedo is the one developped by Huang & Huang, 2023 (ERA5_kernel_alb_TOA. DOI:10.17632/vmg3s67568.3). All the data consistent with the study are gathered in the zip file 'radiative_kernel.zip'.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.012 | 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".