Data 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 “Data for: "Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: regional patterns and uncertainties"" at https://doi.org/10.5281/zenodo.4519583 This dataset is a compilation of eddy covariance and chamber measurements of annual and growing season carbon dioxide (CO2) fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE). The dataset includes flux measurements conducted during 1990–2015 from 148 terrestrial high-latitude tundra and boreal sites. The fluxes and supporting metadata were synthesized by conducting a literature survey, organizing a community call to retrieve unpublished data, and leveraging the available data in FLUXNET2015. Further, we used geospatial products to derive environmental data to these sites. 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 consists of two datasets: annual_growingseason_CO2flux.csv includes the data and annual_growingseason_CO2flux_metadata.csv provides a description of the columns. This dataset can be downloaded at https://doi.org/10.5281/zenodo.4519583
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.052 |
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".