Estimation of Gross Primary Productivity for Global Boreal Forests Using Long Short-Term Memory Neural Networks
Bibliographic record
Abstract
Gross Primary Productivity (GPP) indicates the ability of plants to absorb carbon dioxide from the atmosphere. Accurate estimation of GPP is essential for understanding regional carbon cycles. Boreal forests play an important role in the global carbon cycle and are highly sensitive to global warming, making effective monitoring of their carbon dynamics crucial for mitigating climate change. Nevertheless, significant uncertainties remain in estimating GPP in boreal forests. To address this, this study integrates data from 30 GPP flux sites, along with multiple climate and remote sensing observations, to construct a Long Short-Term Memory (LSTM) neural network model for forecasting and estimating GPP fluxes. Through regional upscaling, we found that from 2015 to 2019, the total GPP in boreal forests showed year-to-year variations, with a mean annual total of 3.22 PgC/year. Spatially, significant photosynthetic activity was primarily concentrated in the northwestern United States and southeastern Canada, as well as most of Europe. This finding provides new perspectives for deepening our understanding of GPP estimation in boreal forests.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".