Based on Atmospheric Physics and Ecological Principle to Assess the Accuracies of Field CO<sub>2</sub>/H<sub>2</sub>O Measurements From Infrared Gas Analyzers in Closed‐Path Eddy‐Covariance Systems
Bibliographic record
Abstract
Abstract Field CO2/H2O measurements from infrared gas analyzers in closed‐path eddy‐covariance systems have wide applications in earth sciences. Knowledge about exactness of these measurements is required to assess measurement applicability. Although the analyzers are specified with uncertainty components (zero drift, gain drift, cross‐sensitivities, and precision), exactness for individual measurements is unavailable due to an absence of methodology to comprehend the components as an overall uncertainty. Adopting an advanced definition of accuracy as a range of all measurement uncertainty sources, the specified components are composited into a model formulated for studying analyzers’ CO2/H2O accuracy equations. Based on atmospheric physics and environmental parameters, the analyzers are evaluated using the equations for CO2 accuracy (±0.78 µmolCO2 mol−1, relatively ±0.18%) and H2O accuracy (±0.15 mmolH2O mol−1). Evaluation shows that precision and cross‐sensitivity are minor uncertainties while zero and gain drifts are major uncertainties. Both drifts need adjusting through zero/span procedures during field maintenance. The equations provide rationales to guide and assess the procedures. H2O span needs more attentions under humid conditions. Under freezing conditions while H2O span is impractical, this span is fortunately unnecessary. Under the same conditions, H2O zero drift dominates H2O measurement uncertainty. Therefore, automatic zero becomes a more applicable and necessary tactic. In general cases of atmospheric CO2 background, automatic CO2 zero/span procedures can narrow CO2 accuracy by 36% (±0.74 to ± 0.47 µmolCO2 mol−1). Automatic/manual H2O zero/span procedures can narrow H2O accuracy by 27% (±0.15 to ±0.11 mmolH2O mol−1). While ensuring system specifications, the procedures guided by equations improve measurement accuracies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".