Bibliographic record
Abstract
1980 is the second year for this experiment, (see 791804). The objectives of this experiment are to determine (1) the threshold of early season spectral detection of soybeans, (2) the spectral response of soybeans as a function of growth and amount of vegetation, and (3) the effect of soil background differences, particularly soil color, on the spectral response and early detection of soybeans. The treatments were as follows: 7 Planting Dates (May 16, 27, June 12, 18, July 7, 16 and 30) 2 Row Spacings (25 and 75 cm) 2 Cultivars (Amsoy, narrow canopy type and Williams, bushy type) 2 Soil Types (Chalmers, darker and Toronto, lighter) A split plot design with two replications was used. Spectral measurements, along with agronomic characterizations of the canopyies and surface soil, were made at approximately weekly intervals throughout the growing season. The spectral reflectance measurements were made with a Landsat band radiometer (Exotech 100). Radiant temperatures and overhead color photographs of the canopies were obtained simultaneously with the reflectance measurements. The major agronomic measurements of the plots included growth stage, percent soil cover, height, leaf area index, biomass, and surface soil moisture and condition. Grain yields were measured at harvest time. Identification Record Codes 1. Level of Factor Codes See document Level of Factor Codes (level_factor_codes_801804.txt) under the Supporting docs tab 2. Experiment Parameters Experiment parameter 09: Air temperature as measured by a probe attached to the boom supporting the multiband radiometer in Celsius degrees. Experiment parameter 10: Radiant temperature as measured by a precision radiation thermometer (PRT-5) obliquely viewing the top surface of the canopy in Celsius degrees. The test took place in 1980. The supporting docs include a brief summary of used instruments, a wavelength table (Wavelength_ASCII.txt), reflectance note and reflectance tables (ReflectanceTable206.txt and ReflectanceTableMulti.txt), and file format description (ExperimentDataFormat3.txt). The format description file is in ASCII format in lines of 80 characters. This research dataset is part the Field Research Data Library that consists of over 200,000 spectral observations of soils and vegetation that have been collected since 1972 till 1991 as part of the research focused on vegetation and soils at the Laboratory for Applications of Remote Sensing (LARS) located at the Purdue University.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.010 |
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".