Patterns and general characteristics of severe forest dieback from 1950 to 1995 in the northeastern United States
Bibliographic record
Abstract
US national and state forest insect and disease surveys provide plentiful information on forest dieback. These data, however, have not been quantified and analyzed systematically to address outstanding questions on the etiology of dieback. This study quantified long-term (19501995) trends in the severity of dieback on Acer saccharum Marsh., Fraxinus spp., Betula spp., and Picea rubens Sarg. in US northern hardwoods. A numeric index (010 scale) of the severity and extent of dieback was applied using key words frequently found in the surveys. The 18 episodes identified showed considerable variability among species at the local scale, yet systematic, repetitive patterns of dieback at the scale of the region and multidecadal time frame. Six dieback characteristics were evident: episodes showed abrupt onset and subsidence, endured 13.6 years on average, were cyclical, with a frequency of 22.3 years between recurrence, and averaged about two-thirds of maximum possible severity. In contrast to the perception that dieback is happenstance and chaotic, this study supports the hypothesis that, by addressing issues of spatial scale and long-term population dynamics, coherent, generic patterns emerge that are cyclic and predictable. Limitations and advantages of the approaches were discussed in terms of meeting needs of the US Forest Health Monitoring Program for innovative approaches to the analysis of the voluminous field data being assembled nationwide. By developing a quantitative database, environmental correlation and modeling of dieback now become possible.
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 | 0.001 | 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) | 0.001 | 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 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".