Bibliographic record
Abstract
Abstract. Although in principle the ECC (Electrochemical Concentration Cell) ozonesonde is an absolute measuring device, in practice it has several “artefacts” which change over the course of a flight. Most of the artefacts have been corrected in the recommendations of the Assessment of Standard Operating Procedures for Ozone Sondes Report (GAW Report No. 268), giving an overall uncertainty of 5–10 % throughout the profile. However, the conversion of sampled ozone into the measured cell current has not been fully quantified, resulting in time-varying background current and pump efficiencies. We describe an updated methodology for ECC sonde data processing that is based on JOSIE 2009/2010 and JOSIE 2017-SHADOZ test chamber data. The stoichiometry (O3/I2) factors and their uncertainties along with the fast and the slow reaction pathways for the different sensing solution types used in the global ozonesonde network are determined. Experimental evidence is given for treating the background current of the ECC-sensor as the superposition of a constant ozone independent component (IB0, measured before ozone exposure in the sonde preparation protocol) and a slow time-variant ozone-dependent current determined from the initial measured ozone current using a first-order numerical convolution. The fast sensor current is refined using the time response determined in sonde preparation with a first order deconvolution scheme. Practical procedures for initializing the numerical deconvolution and convolution schemes to determine the slow and fast ECC currents are given. Calibration functions for specific ozonesondes and sensing solution type combinations were determined by comparing JOSIE 2009/2010 and JOSIE-2017-SHADOZ profiles with the JOSIE ozone reference UV-photometer (OPM). With fast and slow currents resolved and the new calibration functions, a full uncertainty budget is obtained. The time responses correction methodology makes every ozonesonde record traceable to one standard, i.e. the OPM of JOSIE, enabling the goal of a 5 % relative uncertainty to be met throughout the global ozone network.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.023 | 0.012 |
| Insufficient payload (model declined to judge) | 0.309 | 0.257 |
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".