Mapping forest plots: an efficient method combining photogrammetry and field triangulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<ja:p>Intra stand spatial information is often collected in ecological investigations, when functioning or interactions in the ecosystem are studied. Local relative accuracy is often given priority in such cases. Forest maps with accurate absolute positions in a global coordinate system are needed in remote-sensing applications for validation and calibration purposes. Establishing the absolute position is particularly difficult under a canopy as is creating undistorted coordinate systems for large plots in the forest. We present a method that can be used for the absolute mapping of point features under a canopy that is efficient for large forest plots. In this method, an undistorted network of control points is established in the forest using photogrammetric observations of treetops. These points are used for the positioning of other points, using redundant observations of interpoint distances and azimuths and a least squares adjustment. The method provides decimetre-level accuracy and only one person is required to conduct the work. An estimate of the positioning accuracy of each point is readily available in the field. We present the method, a simulation study that explores the potential of the method and results from an experiment in a mixed boreal stand in southern Finland.</ja:p>
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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 | 0.000 | 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 it