Standardizing Pre- and Post-Storm Data Collection for Urban Forestry Research
Bibliographic record
Abstract
Abstract Background To better understand the impacts of storms on urban forests and to develop effective management strategies, it is essential to collect accurate and consistent data on urban forests before and after a storm. However, there is often limited time for researchers to establish sampling protocols and gather data before cleanup efforts erase key visual information, such as failure modes and damage severity. Moreover, storms differ in how frequently and predictably they impact regions, potentially limiting some researchers’ opportunities to gain experience with storm assessment methods. Methods This paper presents a standardized protocol for collecting pre- and post-storm data on urban forests based on previous studies and the authors’ experiences collecting urban tree data following severe storms. Results The protocol covers a wide range of data, including tree species, size, and condition; risk factors; and damage type. The protocol also includes instructions for post-storm data collection using a variety of methods, including field surveys, remote sensing, and citizen science efforts. Conclusions A standardized protocol will help researchers collect consistent data on urban forests before and after storms, while also making the findings more relevant to urban forest managers, as the data will align with what they already collect.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".