Computation of domain‐average radiative flux profiles using Gaussian quadrature
Bibliographic record
Abstract
A method for calculating domain‐average radiative flux profiles, called Gaussian Quadrature Independent Column Approximation (GQ‐ICA), is introduced and assessed using cloud properties retrieved from A‐Train satellite data. This method could be suitable for use in large‐scale atmospheric models. Like the Monte Carlo ICA (McICA), GQ‐ICA uses N stochastically generated subgrid‐scale cloudy columns. The independent variable is the sorted, from smallest to largest, sequence of N sub‐column values of liquid and ice cloud water paths. The integrand is essentially the radiative transfer equation. Accurate GQ integration requires integrands to be relatively smooth functions. Unlike McICA, GQ‐ICA performs full solar and infrared spectral integrations on nG < < N sub‐columns which are identified by rules governing nG‐node GQ. The nG flux profiles are appropriately weighted and summed to give domain averages. Several sorting procedures were considered, and all results are based on the CCCma radiation algorithm. For solar radiation, 1‐node GQ‐ICA can produce significant bias errors, but its random errors are generally less than McICA's. These biases, however, are almost eliminated by 2‐node GQ‐ICA. For GQ‐ICA to better McICA's random errors for infrared fluxes, at least the 2‐node version is needed. Ultimately, 2‐node GQ‐ICA random errors for net fluxes at surface and top‐of‐atmosphere are typically 30–50% of McICA's. This is partly because solar and infrared solvers operate on the same sub‐columns. GQ‐ICA random errors for atmospheric heating rates are comparable to McICA's even for 3‐node GQ‐ICA. Computational times required for the 2‐ and 3‐node GQ‐ICA are, respectively, ∼180 and ∼230% of McICA's.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".