Bibliographic record
Abstract
Abstract—Probability distribution functions (PDFs) can be used to assist in the validation of trace gas retrievals made by satellites. A major advantage of this approach is that large statistical sam-ples are used that do not require correlative measurements to be co-located in space and time. Examples are shown from the launch of UARS through to the present. This approach is also useful to evaluate the consistency among Aura instruments as well as their agreement with other datasets. A key feature of this work is putting the observations of Aura in their long-term historical context via statistical comparisons with previous datasets collected over more than a decade. To validate the Aura data, we use data from a va-riety of platforms including solar occultation (Canadian ACE) and limb sounder satellite instruments, ozonesondes (WOUDC), lidar (NDSC), and aircraft instruments (AVE, PAVE, and MOZAIC). The width of the trace gas PDFs can be used to accurately esti-mate the atmospheric spatial variability (or representativeness un-certainty) of trace gases as a function of time and location. This statistical analysis is also being used as preparation for full Kalman filter chemical assimilations. The analysis is presented online at
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".