Bibliographic record
Abstract
Abstract. The large-scale mapping of plant biophysical and biochemical traits is essential for ecological and environmental applications. Given its finer spectral resolution and unprecedented data availability, hyperspectral data has emerged as a promising, non-destructive tool for accurately retrieving these traits. Machine and particularly deep learning models have shown strong potential in retrieving plant traits from hyperspectral data. However, when deploying these methods at large scales, reliably quantifying associated uncertainty remains a critical challenge, especially when models encounter out-of-domain (OOD) data, such as unseen geographic regions, species, biomes, or data acquisition modalities. Traditional uncertainty quantification methods for deep learning models, including deep ensembles (Ens_UN) and Monte Carlo dropout (MCdrop_UN), rely on the variance of predictions but often fail to capture uncertainty in OOD scenarios, leading to overoptimistic and potentially misleading uncertainty estimates. To address this limitation, we propose a distance-based uncertainty estimation method (Dis_UN) that quantifies prediction uncertainty by measuring dissimilarity in the predictor and embedding space between training and test data. Dis_UN leverages residuals as a proxy for uncertainty and employs dissimilarity indices in data manifolds to estimate worst-case errors via 95-quantile regression. We evaluate Dis_UN on a pre-trained deep learning model for prediction of multiple plant traits from hyperspectral images, analyzing its performance across OOD data, such as pixels containing spectral variation from urban surfaces, bare ground, water, clouds or open surface waters. For this study we target six leaf and canopy traits: Leaf mass per area (LMA), Chlorophyll (Chl), Carotenoids (Car), Nitrogen (N) content, Leaf area index (LAI) and Equivalent water thickness (EWT). Results indicate that Dis_UN effectively differentiates between OOD components and provides more reliable uncertainty estimates than traditional methods, which tend to underestimate the range of uncertainty (on average over traits 26.7 % for Ens_UN and 6.5 % for Dropout_UN). However, challenges remain for traits affected by spectral saturation. These findings highlight the advantages of distance-aware uncertainty quantification methods and underscore the necessity of diverse training datasets to minimize sampling biases and enhance model robustness. The proposed framework improves the reliability of uncertainty estimation in vegetation monitoring and offers a promising approach for broader applications.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.348 | 0.226 |
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".