How many trees are there in the North American boreal forest?
Bibliographic record
Abstract
Boreal forests, the largest terrestrial biome on Earth, are highly varied in local tree density. Despite previous attempts to estimate tree density in boreal forests, the accuracy of the estimation is unknown, leaving the question how many trees there are in boreal forests largely unanswered. Here, we compiled tree density data from 4367 plots in North American boreal forest and developed tree height‐based generalized linear and machine learning models to address this question. We further produced the current boreal tree density map of North America, and projected tree density distribution in 2050 under the shared socioeconomic pathways (SSP) 126, 245 and 585 climate change scenarios. Our best‐performed and cross‐validated random forest model estimated a total of 277.2 (± 137.7 SD) billion trees in the North American boreal forest, 31.3% higher than the previously estimated 211.2 billion. Our projected tree density distributions in 2050 showed at least 11% increase in tree density in the region. This study improves our knowledge about boreal tree density and contributes to understanding the role of boreal forests in regulating forest ecosystem functions and informing adaptation and mitigation policy‐making. The projected warming‐induced increase in tree density suggests the potential of the North American boreal forest for carbon sequestration.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".