Assessing the quality of forest fuel loading data collected using public participation methods and smartphones
Bibliographic record
Abstract
Effective wildfire management in the wildland–urban interface (WUI) depends on timely data on forest fuel loading to inform management decisions. Mobile personal communication devices, such as smartphones, present new opportunities to collect data in the WUI, using sensors within the device – such as the camera, global positioning system (GPS), accelerometer, compass, data storage and networked data transfer. In addition to providing a tool for forest professionals, smartphones can also facilitate engaging other members of the community in forest management as they are now available to a growing proportion of the general population. Approaches where the public participates in the data-collection process (inspired by citizen science) may be beneficial for fire hazard issues. This research note demonstrates a smartphone application for measuring forest fuel loading in the WUI by forestry professionals and non-professionals, and evaluates the quality of the collected data. Smartphones and their associated applications may provide new tools for collecting forest structural data in the WUI, but forest managers need to ensure that measurement protocols provide the required precision for analysis and enforce the logical consistency of observations made by a diverse set of data collectors, and that sufficient training is provided. If these recommendations are followed, we conclude that data acquired by volunteers in collaborative projects through smartphone applications can be of acceptable quality to help inform forest management decisions.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".