Study on the Relationship between Hyperspectral Polarized Information of Soil Salinization and Soil Line
Bibliographic record
Abstract
It has important significance to assess soil salinization correctly for agricultural production and ecological environment.Soil line can indicate soil salinization in a certain extent.But the soil spectral characteristics obtained at different angles will change with the changing of the soil line parameters.Base on polarized hyper-spectral reflectivity obtained in the laboratory,the study analyzes the relationship between the soil salinization and soil line parameters,explores preliminarily the best way to obtain soil line.The results show:(1)Soil spectral reflectance gradually increased slowly with increasing band.With the enhanced level of salinization,soil spectral reflectance of the first to be gradually reduced to a critical value and then gradually increased.(2)Soil salinization has a linear correlation with the soil slope and intercept.With the enhanced level of salinization,soil slope becomes smaller,and intercept becomes larger.(3)Viewing zenith angle affects the relationship between the polarization state and soil line parameters.When viewing zenith angle is fixed,there is a regularity between the polarization state and soil line parameters.When the viewing zenith angle is between 0°~50°,with the angle becoming larger,soil slope becomes larger,and intercept becomes smaller.(4)Polarization states affects degree of correlation between soil salinization and soil line parameters.When polarization angle is 90°and viewing zenith angle is 25°,the relationship model between soil salinization and soil line parameters is better.The research results can be used to evaluate the degree of salinization soil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".