Bibliographic record
Abstract
Reports have identified changes in abiotic factors that potentially affect forest growth. A synthesis of studies of thesechanges in Canada over the past century was undertaken to evaluate how these factors may be influencing forest growth.Reviewed papers used multiple sources of data including long-term inventory plots, tree-ring reconstructions, historicalgeographic data, and forest growth models. The synthesis showed that several positive growth trends were found inBritish Columbia and eastern Canada, while results from the western interior of Canada were mixed. Trembling aspen(Populus tremuloides Michx.) dieback has been noted due to severe and prolonged drought events, with growth reduc-tions and mortality also documented for conifers in the western interior. Studies have also found slow forest expansionin many areas and at the northern tree-line. Overall, authors attributed positive forest growth trends to rising CO 2 con-centrations, N deposition, increased precipitation, and increased temperature. Growth declines were generally attributedto a combination of increased temperatures and reduced precipitation. Studies also differed due to time periods consid-ered and how age effects were corrected. Methodological issues were identified that led to contradictory results betweensome studies. These issues need further study.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".