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
Abstract Stem maps describing the spatial location of trees sampled in a forest inventory are used increasingly to model relationships between neighboring trees in distance-dependent growth and yield models, as well as in stand visualization software. Current techniques and equipment available to acquire tree spatial locations prohibit widespread application because they are time-consuming, costly, and prone to measurement error. In this report, we present a technique to derive stem maps from a series of digital photographs processed to form a seamless 360° panorama plot image. Processes are described to derive distance from plot center and azimuth to each plot tree. The technique was tested on 46 field plots (1,398 sample trees) under a range of forest conditions and compared with traditional methods. Average absolute distance error was 0.38 ± 0.44 m, and average absolute azimuth error was 2.3 ± 2.5°. Computed average horizontal accuracy was 0.40 ± 0.42 m, with 85% of measured trees being within 0.5 m of the field-measured tree location.
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.
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 | 0.000 | 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) | 0.001 | 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