Random Forest Development and Modeling of Gross Primary Productivity in the Hudson Bay Lowlands
Bibliographic record
Abstract
Peatlands are a critical component of the global carbon cycle. Within Canada, the Hudson Bay Lowlands (HBL) has accumulated an estimated 33 Gt of carbon as peat because of a small but persistent difference between gross primary productivity (GPP) and ecosystem respiration over millennia. However, the impacts of disturbance and climate change on GPP are difficult to monitor across the HBL due to its vast size and remote location. This study evaluates the potential for random forest regression models to estimate GPP at five HBL eddy covariance flux monitoring sites using only optical data from MODIS (500 m, 8 day) or harmonized Landsat/Sentinel (HLS; 30 m, 16 day or more frequent). The results show that spatial resolution has less impact on modeled daily GPP compared to temporal resolution across model configurations. Using MODIS data, individual sites’ daily GPP could be simulated with minimal bias, R2 up to 0.89 and mean absolute error as low as 0.37 g C m−2 day−1. For annual GPP, MODIS (R2 = 0.84; mean absolute error 40.5 g C m−2 year−1) also outperformed the HLS models (R2 = 0.46; mean absolute error 86.4 g C m−2 year−1).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".