Prediction of lignin content of plantation poplar using near infrared spectroscopy method
Bibliographic record
Abstract
The lignin contents of plantation poplar were estimated using the method of the near infrared. The lignin contents of 42 samples of poplar were determined by national standard of China,and then the near infrared( NIR) of all samples were collected by LabSpec Pro FR/A114260 in this paper. The calibration and validation model were built using PLS1,PLS2 and PCR with different pretreatment methods of no-pretreatment. Baseline,the first derivative and the second derivative in different spectral region of 350-2 500 nm,1 300-2 050 nm and 2 050-2 500 nm. The result shows that the model is the best using PLS2 with no-pretreated of spectral data and 10 principal components in 1 300-2 050nm. The coefficients of correlation( r),the root mean square error and the standard error of calibration model are0. 968 5,0. 006 4 and 0. 006 6,respectively,and 0. 655 3,0. 020 2 and 0. 020 5 for validation model. The correlation( r) is 0. 766 5 between the prediction and lab measuring values of the samples without involved in modeling.
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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 | 0.000 |
| 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".