Bibliographic record
Abstract
Changes over the past century in factors such as temperature, precipitation, fire regimes, ozone, atmospheric CO2, and nitrogen deposition naturally lead to questions about forest growth over this same time period. Determining changes in forest growth over long intervals is complicated by constantly changing growth conditions due to tree maturation, stand self-thinning, disturbance, fires, and other factors. Because a comprehensive review is lacking, results were evaluated from publications examining forest growth trends in the eastern United States over the past 100 years. Available studies used multiple sources of data, including permanent plots, growth models, and tree-ring analysis to evaluate forest growth trends. Reviewed publications (n = 19) reported medium to strong growth enhancement based on a variety of data types over periods exceeding 100 years in some cases. Model-based analyses, which mostly did not include CO2 and nitrogen fertilization effects, had lower estimates of growth enhancement. Results were consistent for different study lengths and data types. No study reported forest-scale growth declines. Factors identified as the cause of enhanced growth varied by study, but included rising CO2 concentrations, N deposition, increased precipitation, and warming temperatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".