AN IMPROVED BOOSTING-BIPLS MODELS BASED ON WEIGHT ADJUSTMENT FOR SOIL HEAVY METAL CONTENT PREDICTION
Bibliographic record
Abstract
Heavy metal pollution in soil has got more and more attention, and X-ray fluorescence spectroscopy analysis is a widely used method for heavy metal content in soil.The establishment of accurate model is helpful for the rapid detection of heavy metal content.Firstly, eight spectrum pretreatment methods are used before modelling, and the pre-processing method of least squares which improved multi-scatter correction is chosen.Secondly, Boosting-backward interval partial least squares (Boosting-BiPLS) model is established which combines several basic models with different characteristics into a strong one to solving the "building nesting effect of BiPLS.Then from bias-oriented model, an improved Boosting-BiPLS model is proposed, in which the weight of samples is adjusted on the basis of the relative deviation of the samples and the weight of base models is dynamically calculated by the spectral similarity.Finally, to prove the effectiveness of the improved model, the improved Boosting-BiPLS model is compared with the traditional Boosting-BiPLS model.The results show that the correlation coefficients of the five heavy metal elements of the improved Booting-BiPLS model are all about 0.99, and the average relative deviations are all <10%, with the prediction accuracy of Boosting-BiPLS improved by more than 50%.Moreover, the model is more stable.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".