The Total Carbon Column Observing Network's GGG2020 Data Version: Data Quality, Comparison with GGG2014, and Future Outlook
Bibliographic record
Abstract
New atmospheric gas retrievals using the GGG2020 version of the Total Carbon Column Observing Network (TCCON) dataset are significantly improved compared with the previous GGG2014 data version. The GGG2020 retrievals have lower variability across the network, better spectral fits, and smaller biases. The exception to this is XCH4 , which is, on average across the network, 5.3 ppb lower in GGG2020 than in GGG2014, a difference we mostly attribute to the a priori profiles used to perform the scaling. We discuss the remaining biases and corrections, the TCCON approach of identifying, quantifying, and minimizing biases, and suggest future work to further minimize the biases. The GGG2020 TCCON data are available from the TCCON archive at http://tccondata.org.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".