Unrestricted guided transect sampling for surveying sparse species
Bibliographic record
Abstract
We present a modification of an earlier presented method using prior auxiliary information in the layout of survey strips. The idea is to imitate a skilled surveyor who purposively seeks the species of interest. Yet, the method “unrestricted guided transect sampling” (UGTS) is a probability sampling method. In comparison with a strip survey using no auxiliary information, UGTS gave 11%–64% lower standard errors for estimates of species population size in three simulated forest types. In a test in six stands where European aspen ( Populus tremula L.) and an epiphytic moss ( Orthotrichum speciosum Nees) had been mapped, UGTS gave a small improvement in some stands but considerably higher standard errors in other stands with kNN estimates of volume of deciduous trees derived from satellite images as covariate values. With covariates values simulated from aspen basal area, UGTS gave 8%–75% lower standard error than a strip survey using no auxiliary information. The study shows a gain in precision by using auxiliary information both in the design and in estimation when surveying sparse species but also that the correlation between the covariate and the variable of interest has to be relatively strong to make the method worthwhile.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".