Testing the efficiency of structured and unstructured surveys for detecting a small population of Jack in the Pulpit (<i>Arisaema triphyllum</i>) plants
Bibliographic record
Abstract
Detecting inconspicuous species with low abundance is challenging, yet failure to detect these species can have important conservation implications. Few experimental studies have been conducted to assess how survey protocols affect plant species detectability. We tested the efficiency of structured (transect-based) and unstructured (unplanned search) surveys for the detection of a small, simulated population of Jack in the Pulpit ( Arisaema triphyllum) plants. We found no significant difference in the mean percentage of plants detected between survey types (27% structured, 31% unstructured, 95% CI = −0.23, Inf). However, unstructured surveys detected slightly more plants per unit time, while structured surveys covered a slightly larger portion of the study area. Participants located 33% of plants across all 18 surveys. The probability of finding all plants in a single survey was very low (7.59 × 10–4 to 8.89%). To ensure a 95% probability of detection of at least one plant in our study, a minimum of two to four surveys would be required. These results suggest that for population estimation or presence/absence surveys, particularly for species of conservation concern with low abundance or invasive species at the early stages of invasion, a single survey might not be enough.
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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 | 0.030 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".