Variability and bias in likelihood of urban tree failure ratings
Bibliographic record
Abstract
Tree risk assessors consider the likelihood of failure (LoF), the likelihood of impact, and the consequence of failure. Despite the need for replicable assessments, variability in LoF ratings among assessors remains underexamined. We designed an online survey that included photos and descriptions of nine trees, including different scenarios to test whether LoF ratings changed with factors like the presence of targets or based on the client’s level of concern. A total of 702 Canadian and US tree risk assessors participated. We found that qualitative ratings can be associated with quantitative ratings as Improbable: ≤ 24%; Possible: 25% - 54%; Probable: 55% - 79%; and Imminent: ≥ 80%. LoF rating severity decreased with increasing latitude and amongst ISA Tree Risk Assessment Qualified (TRAQ) credential holders but increased with the perceived risk of walking alone at night, one of the three health risk perceptions tested in the study. LoF rating deviation deceased amongst TRAQ credential holders but increased alongside years of experience and risk perception of not wearing a seatbelt while driving. LoF ratings generally did not differ between tree scenarios, except for Trees 7 and 9 scenarios. Despite viewing the exact same photos, respondents told to retrospectively assess LoF after the limb had failed rated the LoF higher than the Control group (non-retrospective ratings). When examining the qualitative ratings, Tree 9 scenarios remained significant, and Tree 7 became significant with qualitative ratings increasing when the client was concerned about their tree. The findings support training to improve assessment replicability and reliability. • Surveyed 702 tree risk assessors on likelihood of failure ratings for nine trees • Identified relationship between quantitative (0-100%) and qualitative (TRAQ) ratings • ISA Tree Risk Assessment Qualified assessors had lower rating severity and deviation • Rating severity and deviation increases with increased health risk perceptions • Retrospective assessments and concerned clients increased rating severity
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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 | 0.048 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".