Bibliographic record
Abstract
A case is made for the detection of melting snow or ice using multispectral remote sensing from earth satellites. Snow and thick ice are highly reflective in both the visible and the near-infrared portions of the clectrornagnetic spectrum. During thaw conditions, however, near-infrared radiation is absorbed strongly, while reflection of visible radiation is only slightly affected. Simultaneous visible and near-infrared imagery from the Nimbus 3 satellite illustrates how these reflectance differences can be used to obtain information of hydrologic usefulness. Two examples of such use arc presented. Hydrologic information from earth satellites has been increased by the addition to the Nimbus 3 meteorological satellite of instrumentation to obtain solar reflectance imagery from the near-infrared. Several studies (Mac-Lead 1971, M ~ 1970, ~ B~~~ ~ L and ~~~~~l~ ~ ~ 1970) ~ have indicated potential hydrologica ~ of these data for obtaining information on drainage basins, Snow cover, plant distribution, and flood extent. use of the nearinfrared near-^^) data in with the visible spectrum imagery appears to permit the detection of thawing and ice packs. 'p'wo examples of use, Over the Lake Winnipeg region of Canada and the Sierra Nevada of California and Nevada, are presented in this report. The experimental Nimbus 3 meteorological satellite, launched Apr. 14, 1969, provided for 24-hr mapping of the global cloud cover and for nighttime infrared measurements of earth surface and cloud top temperatures in the 3.6- to 4.2-pm portion of the spectrum. During the day, the High Resolution Infrared Radiometer (HRIR) complements the television coverage (0.5-0.7 pm) of the Image Dissector Camera System (IDCS), by measuring reflected shortwave radiation in the 0.7- to 1.3-pm spectral region. The area instantaneously viewed by the HRIR scanner at nadir is about 8.5 km in diameter, a resolution somewhat coarser than the 4.1-km resolution of the IDCS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.768 | 0.684 |
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".