Final report for the project "Improving the understanding of surface-atmosphere radiative interactions by mapping surface reflectance over the ARM CART site" (award DE-FG02-02ER63351)
Bibliographic record
Abstract
Surface spectral reflectance (albedo) is a fundamental variable affecting the transfer of solar radiation and the Earth’s climate. It determines the proportion of solar energy absorbed by the surface and reflected back to the atmosphere. The International Panel on Climate Change (IPCC) identified surface albedo among key factors influencing climate radiative forcing. Accurate knowledge of surface reflective properties is important for advancing weather forecasting and climate change impact studies. It is also important for determining radiative impact and acceptable levels of greenhouse gases in the atmosphere, which makes this work strongly linked to major scientific objectives of the Climate Change Research Division (CCRD) and Atmospheric Radiation Measurement (ARM) Program. Most significant accomplishments of eth project are listed below. I) Surface albedo/BRDF datasets from 1995 to the end of 2004 have been produced. They were made available to the ARM community and other interested users through the CCRS public ftp site ftp://ftp.ccrs.nrcan.gc.ca/ad/CCRS_ARM/ and ARM IOP data archive under “PI data Trishchenko”. II) Surface albedo properties over the ARM SGP area have been described for 10-year period. Comparison with ECMWF data product showed some deficiencies in the ECMWF surface scheme, such as missing some seasonal variability and no dependence on sky-conditions which biases surface energy budget and has some influence of the diurnal cycle of upward radiation and atmospheric absorption. III) Four surface albedo Intensive Observation Period (IOP) Field Campaigns have been conducted for every season (August, 2002, May 2003, February 2004 and October 2004). Data have been prepared, documented and transferred to ARM IOP archive. Nine peer-reviewed journal papers and 26 conference papers have been published.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.154 | 0.061 |
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".