Sampling with probability proportional to prediction: rethinking rapid plant diversity assessment
Bibliographic record
Abstract
Rapid biodiversity assessment (RBA) methods are regularly applied to assess plant species richness. One approach is developing sampling designs that integrate expert knowledge. 3P sampling does so by selecting samples with probability proportional to prediction (3P). Higher effort is allocated to areas with high species richness based on predictions made on the ground. 3P sampling for RBA was simulated considering two major factors: knowledge of plant species and types of rapid assessment. Two large census forest plots over 25 ha in size were used. Results showed that sampling error of 3P sampling for RBA was relatively low and could be improved by changing methods of prediction. Sampling was more efficient and accurate when predictions were made with knowledge about abundant species instead of random species. When such prediction was made, knowing only three quarters of the total species richness in a forest performed as well as full knowledge. Randomly walking around in an area and predicting also increased efficiency and accuracy compared to standing stationary at an assessment point. This was counterintuitive to the common practices of establishing ground plots for assessment. Our findings propose that 3P sampling for RBA is workable through engaging local communities in an assessment, which could be cost-effective. Finally, the procedure laid out in this study is the first unequal probability sampling design proposed for RBA.
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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 | 0.003 | 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 | 0.002 | 0.001 |
| 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".