Checking the performance of point and plot sampling on aerial photoimagery of a large-scale population of trees outside forests
Bibliographic record
Abstract
The present study investigates some sampling strategies for the estimation of abundance and canopy cover of trees outside forest (TOF) over large areas. A collection of about 53 000 TOF units in Central Italy was acquired by visual, on-screen interpretation of aerial orthophotos and was taken as the reference population with the purpose of investigating: (i) one-phase inventories with sample points located by means of the tessellation stratified sampling (TSS), which involves covering the study region by a grid of regular polygons of equal sizes and randomly and independently selecting a point in each of them; (ii) two-phase inventories with the one-per-stratum stratified sampling adopted in the second phase to select a sample of polygons from the grid and then visit only the points contained in those polygons. Uniform random sampling is also considered in the first phase as a benchmark for tessellation stratified sampling. The sampling schemes adopted to select TOF units at the sample points are as follows: (i) point sampling, (ii) centroid-based plot sampling with plot radius of 50 m (CPLS50) or 100 m, and (iii) plot intersect sampling with plot radius of 50 or 100 m. CPLS50 under single-phase TSS proves to be a promising strategy to large-scale TOF inventories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 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 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".