An Evaluation of Bur Oak (<i>Quercus macrocarpa</i>) Decline in the Urban Forest of Winnipeg, Manitoba, Canada
Bibliographic record
Abstract
Abstract Winnipeg, Manitoba, Canada, has a large, indigenous population of bur oak (Quercus macrocarpaMichx.). In the 1980s, many of these trees were showing signs of decline, a disease caused by a complex of abiotic and secondary biotic stressing agents. Potential causal factors were investigated by comparing various aspects of 120 bur oaks visually rated as healthy or declined based on crown dieback levels. The results indicated that many selected bur oak trees predated surrounding urban development and that declined trees were significantly older with more severe stem wounds and competition from surrounding trees than healthy specimens. Average annual growth ring widths of healthy and declined trees were similar in the early part of the 20th century. However, decline actually began decades before symptoms were noticed, coinciding with a period of intense city-wide urban development, as growth of declined trees was slower than that of healthy trees beginning sporadically in the 1940s and consistently from 1974 to 2001. During the early years of decline, the year-by-year separation in ring width between the two categories was significantly positively related to precipitation levels. This suggested that in wet years, declined trees may have been surrounded by unfavorable water-logged soils, possibly as a result of natural drainage patterns being impeded by urban development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".