Multiplicative Scatter Correction and Stepwise Regression to Build NIRS Model for Analysis of Soil Organic Carbon Content in Black Soil
Bibliographic record
Abstract
Near infrared modeling based on the whole spectroscopy takes much work and the model may not be optimum because of too much tedious information.Wavelength selection through certain method could improve the model.Stepwise regression is usually used to select wavelengths with important information.Multiple scatter correction(MSC) technique can be used effectively to remove the effect of scatterings due to physical factors such as the density and humidity of samples and other factors caused by operators.A total of 136 Black Soil samples were obtained during 2004-2005 in Northeast China and corresponding infrared spectra from 3699-12000 cm-1 was measured using Fourier transform infrared spectrometry.Multiple scatter correction was used to preprocess the original spectra and multiple stepwise regression method was used to select wavelengths.Results showed that MSC can effectively decrease the scattering effect and thus enhance the signal to noise ratio.Compared to original spectrum,MSC improved the SOC quantitative model,with the determination coefficient increased from 0.598 to 0.681.Determination coefficients of model based on wavelengths chosen by experience was 0.956.Model based on wavelengths selected by hand was better than that based on wavelengths selected by stepwise regression analysis.Selecting wavelengths by stepwise regression analysis needs to be further studied.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".