A comparison of solar radiation budgets in the Mackenzie river basin from satellite measurements and a regional climate model
Bibliographic record
Abstract
Using a new narrowband to broadband conversion algorithm developed specifically for the Mackenzie River basin (MRB), Advanced Very High Resolution Radiometer (AVHRR) data for the summer of 1994 have been analyzed to obtain the top‐of‐the‐atmosphere (TOA) fluxes and the net surface solar radiation in the MRB. The AVHRR dataset contains mid‐afternoon scenes from 21 June to 14 September 1994. In addition, the Scanner for Radiation Budget (ScaRaB) instrument, which flew on the METEOR‐3 satellite, provided good coverage during the summer of 1994 with typically five passes over the northern part of the basin and three to four passes over the southern part of the basin each day. We merged the TOA fluxes from AVHRR with those from ScaRaB and then interpolated the observations (within ± 1.5 hours) to get the monthly means for each one‐hour period during the daytime for June, July, August and September 1994. The monthly means of each one‐hour average are compared with output from the Canadian Regional Climate Model (CRCM). The comparison shows that the CRCM simulated the TOA reflected fluxes well over the MRB. However, the differences in the partitioning of the absorbed energy between the surface and the atmosphere are large, the CRCM overestimating the surface net solar radiation budgets by about 15%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".