Using Ensemble-Based Systems with Near-Infrared Hyperspectral Data to Estimate Seasonal Snowpack Density
Bibliographic record
Abstract
Estimating the seasonal density of the snowpack has many financial and environmental benefits. Rapid assessment and daily monitoring of its evolution are therefore key to effective prevention. Traditionally, the physical characteristics of snow are measured directly in the field, which involves high costs and personnel mobilization. Hyperspectral imaging is a reliable and efficient technique to study and evaluate this physical property. The spectral reflectance of snow is partly defined by changes in its physical properties, particularly in the Near infrared (NIR) part of the spectrum. Recently, a hybrid snow density estimation model allowing retrieval of density from NIR hyperspectral data was developed, based on an a priori classification of snow samples. However, in order to obtain optimal density estimates with the Hybrid model (HM), the sources of classification and estimation error must be controlled. Following the same principle as the HM, an Ensemble-based system (EBS) was developed. This model reduces the number of misclassification errors produced by the HM. The general concept of EBS algorithms is based on the principle that obtaining more opinions before making a decision is part of human nature, especially when economic and environmental benefits are at stake. This approach has helped to reduce the risk of classification and estimation errors and to develop more robust density results. One hundred and fourteen snow samples collected during three winters (2018–2020) were used to calibrate and validate the EBS. The performance of the EBS was validated using an independent database and the results were satisfactory (R2 = 0.90, RMSE = 44.45 kg m−3, BIAS = 3.87 kg m−3 and NASH = 0.89).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".