An evidence map and guide for using community science, remote sensing, and environmental <scp>DNA</scp> for rare plant detection
Bibliographic record
Abstract
Abstract Field surveys have been the standard method for conducting species monitoring. However, other methods involving data from community science, remote sensing, and environmental DNA (eDNA) are increasingly being recognized for their potential as complements to traditional surveys. This evidence map examines studies that use these techniques for detecting rare terrestrial vascular plants. We explore plant types detected, study habitats, and recommendations for future research. We also use a case study of Canadian plant species at risk to suggest species that might potentially benefit from being detected via these techniques. We find that herbaceous species are the most common vascular plant type detected in community science and eDNA studies, while trees dominate remote sensing studies. Most community science studies occurred in urban areas, while most remote sensing studies occurred in areas that comprised multiple habitats, and most eDNA studies occurred in semi‐open habitats such as shrublands and wetlands. Few studies discussed the efficacy of these techniques in terms of detection success and resources used. To better situate these new techniques among monitoring options, we discuss common problems, potential solutions, sources of false negative and false positive errors, and financial cost considerations for each technique.
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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 | 0.027 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.029 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".