Predictions for "Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: regional patterns and uncertainties"
Bibliographic record
Abstract
This record is for the dataset “Predictions for "Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: regional patterns and uncertainties” at https://zenodo.org/doi/10.5281/zenodo.4521851. Description of the predictions These data include predictions of annual and growing season carbon dioxide (CO2) fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990–2015 across the terrestrial high-latitude tundra and boreal region. We synthesized flux measurements and used geospatial data to predict (i.e., upscale) CO2 fluxes at relatively high spatial resolution (1 km2) across the high-latitude region using five commonly-used statistical models and their ensemble, i.e., the median of all five models. Predictions were made separately for each year and flux. More details can be found in Virkkala et al. "Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: regional patterns and uncertainties" (in review) Files in this repository This repository includes 27 rasters for each flux variable (i.e., annual GPP, annual ER, annual NEE, growing season GPP, growing season ER, growing season NEE). These raster files represent ensemble predictions for each flux variable and year (from 1990 to 2015, 26 years in total), and an average prediction across the years. Annual and growing season rasters are found in their own zipped folders. Rasters are in Lambert North Pole Equal-Area Projection and in .tif format and come together with their supporting files which are useful for plotting the maps in e.g. ArcMap. Permanent water bodies and croplands were masked from these predictions. Quality control of the predictors resulted in some pixels lacking values for a given year, resulting in slight inconsistencies in data set extent across the years. Flux predictions need to be multiplied by 0.01 to arrive at the original scale This dataset can be downloaded at https://zenodo.org/doi/10.5281/zenodo.4521851.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".