Bridging knowledge gaps with hybrid machine-learning forest ecosystem models (ML-FEMs): inferential simulation of past understory light regimes
Bibliographic record
Abstract
Soil moisture is a key limiting factor of plant productivity in boreal and montane regions, producing additional climate feedbacks through evaporation, regeneration, mortality, and respiration. Understory solar irradiation – the primary driver of surface temperature and evaporative demand – remains poorly represented in vegetation models due to a lack of 3-D canopy geometry. Existing models are further unable to represent processes lacking sufficient parameterization and/or knowledge, with no land model to date utilizing machine learning (ML) to represent vegetation processes. Here, we developed the first hybrid forest ecosystem model using ML (ML-FEM), a specific case of hybrid AI land model (a concept also invented here). In this approach, ML models are trained and validated with a ground-truth dataset, whether observations or high-fidelity simulations, before being applied to vegetation model parameters for inference, internally or externally to the model. Using this approach, we simulated annual understory global solar irradiation (Iu) across 25.2 Mha in southwestern Canada at 1-ha resolution under historical climate and fire scenarios. In cross-validation, we found that linear and ML regression models performed comparably well in the prediction of angular canopy cover (ACC), due to the linearity of its relationship to predictors (linear R2 = 0.938, RMSE = 0.079; ML R2 = 0.939, RMSE = 0.074). Reduced area burned, increased ignitions, and reduced regeneration potential for recent periods resulted in stable or reduced Iu. This suggests that diminished disturbance may reduce Iu through forest aging, masking latent regeneration decline. Only in the most extreme and unconstrained scenarios did Iu increase. In these experiments, conducted in late 2015, we demonstrated an entirely new class of hybrid models that we anticipated to be of vital importance to understanding and representing pattern-based processes in Earth system models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".