Pollen sleuthing for terrestrial plant surveys: Locating plant populations by exploiting pollen movement
Why this work is in the frame
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Bibliographic record
Abstract
PREMISE OF THE STUDY: We present an innovative technique for sampling, identifying, and locating plant populations that release pollen, without extensive ground surveys. This method (1) samples pollen at random locations within the target species' habitat, (2) detects species' presence using morphological pollen analysis, and (3) uses kriging to predict likely locations of populations to focus future search efforts. METHODS: in an old field. Population size varied from 0-100 flowers labeled with artificial pollen (paint pellets). After characterizing the landscape, we pan-trapped 2762 potential insect vectors from random locations across the field and washed particulate matter from their bodies to assess artificial pollen abundance with a microscope. RESULTS: Population size greatly influenced artificial pollen detection success; following random pollen trap sampling and interpolation, ground surveys would be best focused on identified areas with high pollen density and low variation in pollen density. Sampling sites most successfully detected artificial pollen when they were located at higher elevations, near showy flowering plants that were not grasses. DISCUSSION: Detection of nascent populations using the proposed system is possible but accuracy will depend on local environmental factors (e.g., wind, elevation). Conservation and invasive species control programs may be improved by using this approach.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it