Retrieving heavy metal concentrations in urban soil using satellite hyperspectral imagery
Bibliographic record
Abstract
Efficient prediction and precise depiction of heavy metal concentrations in urban soil are essential for mitigating non-point source pollution and safeguarding public health. Therefore, this research investigated the estimation of soil heavy metal concentrations derived from Gaofen-5 (GF-5) hyperspectral images calibrated by the direct standardization (DS) algorithm. The inversion strategy for soil heavy metal concentrations in response to the two-dimensional soil spectral index (2D-SSI) was proposed by coupling Pearson correlation coefficient (r) and competitive adaptive reweighting algorithm (CARS) for feature selection. The results indicated that the optimal models based on 2D-SSI outperform the models based on calibrated, filtered original spectral bands. For Pb, Cu, Cd, and Hg, the optimal model determination coefficients for the validation data set (RV2) were 0.871 (SVM), 0.883 (BPNN), 0.834 (PLSR), and 0.907 (PLSR), respectively. The spectral features were highlighted in the two-dimensional feature space, and the predicted distribution of heavy metal concentrations was aligned with the observed ground measurements. This study revealed that the prediction strategy based on DS-corrected GF-5 AHSI images with constructed 2D-SSI features can serve as a reliable technical approach for soil heavy metal prediction and pollution prevention.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".