Experimental design for spatial sampling applied to the study of tropical forest regeneration
Bibliographic record
Abstract
For practical reasons, estimating seed production in tropical forest is only possible by sampling. Classical sampling designs (random or systematic) give poor estimations of seed abundance. The spatial disposition of the trees, combined with nonuniform seed dispersal, leads to a highly heterogeneous spatial distribution of the seeds. We propose a random stratified sampling design based on a model that takes account of seed dispersal processes and the location of the trees. We assume a gamma distribution for dispersal distances. The overall seed dispersal area is divided into adjacent quadrats. In each quadrat, the number of seeds follows a Poisson distribution with the mean derived from the model. We estimate model parameters from the results of a previous study and give the variance of the HorvitzThompson estimator of population total for stratified and random sampling designs. A simulation study is used to find the optimal number of strata, and the performance of the sampling design is evaluated. For each model, we compared the variance of the estimator of population total obtained with the stratified sampling design with that obtained with the random sampling design with the same sample size. The stratified sampling design is, on average, 25 times as precise as the random sampling design.
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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 | 0.069 | 0.098 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".