Capturing constraints on boreal gross primary productivity using the remote sensing-based CAN-TG model.
Bibliographic record
Abstract
In response to the limited number and distribution of in-situ carbon flux observations, remote sensing-based methods are increasingly relied upon for the estimation of Gross Primary Productivity (GPP) at regional to global scales. These remote sensing-informed estimates are commonly derived through process-based modelling frameworks which prescribe functional relationships between model inputs and target GPP. Across highly heterogeneous landscapes like the Canadian boreal, these parameters are difficult to constrain and often site-specific. Recent work has determined that parameterization alone may not improve model performance, instead requiring additional model inputs to capture the complex drivers of vegetation productivity across land cover types. In response to these challenges, we applied the remote sensing-based CAN-TG framework to estimate boreal GPP, leveraged through a random forest (RF) machine learning approach that does not assume linear or functional relationships between input variables and productivity. Stratified by land cover, fire disturbance history, and topography, models were assessed for their ability to capture reference GPP from NASA's complex, process-based Soil Moisture Active Passive (SMAP) GPP product. Across all boreal strata, model r 2 values ranged from 0.93 to 0.96, demonstrating that the variability in substantially more complex models can be successfully captured using a simple, interpretable remote sensing-based framework. Through the addition of remote sensing variables capturing freeze/thaw and soil moisture dynamics to surface temperature and greenness, the CAN-TG model demonstrated an improved ability to capture GPP compared to a benchmark GPP model. Seasonal RF models across key boreal land cover, fire disturbance history and topographic strata further demonstrated varying and complex non-linear relationships between model variables and GPP. Spring and fall models generally outperformed winter and summer models, reaffirming model strengths whilst also highlighting remaining uncertainty and areas for future model improvement.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".