Considering regeneration failure in the context of changing climate and disturbance regimes in western North America
Bibliographic record
Abstract
Wildfire-mediated changes to forests have prompted numerous studies on post-fire forest recovery of coniferous forests. Given climate change, a growing body of work demonstrates that conifer regeneration in temperate and boreal forests is declining, a phenomenon often termed “regeneration failure.” However, the definition and parameters are numerous and variable. Characterization of drought also varies greatly, thus hindering the ability to compare results among areas. This review discusses new perspectives on conifer regeneration failure and places these studies into the context of drought and fire activity. We focus this review on three forest types where conifer regeneration failure is well documented: western boreal forests, cold mixed-conifer forests, and dry pine forests. To place the challenges to conifer tree regeneration in the context of regional climate trends, we present a novel regional analysis that summarizes drought conditions prior, during, and following the year of a large wildfire. We demonstrate the need to assess failure in the context of specific forest dynamics and well-defined metrics. For example, tree establishment may historically occur over longer periods, and current and future climate may exacerbate this and not promote pre-fire forest structure and composition. Many forests are undergoing rapid change and the type, magnitude, and causes of changes need to be compared among areas. As such, we should be cautious of quantifying “regeneration failure” and drought without providing spatial and temporal context.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".