Spatial variation in reference conditions: historical tree density and pattern on a Pinus ponderosa landscape
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
The reference conditions of historical tree density and pattern underpin ecological restoration and management of Pinus ponderosa Douglas ex Lawson & C.Lawson forests in western North America, yet the potential spatial variation in these variables across the landscape remains unclear. We reconstructed historical (1880) tree density and spatial pattern on 1 ha plots at 53 sites within a 110 000 ha P. ponderosa landscape in northern Arizona, compared these variables among US Forest Service ecosystem classification units, and modeled spatial variation with environmental variables. Mean tree density differed 19-fold among nine ecosystem types, and regression trees using four soil or climatic variables explained 62%–74% of the variation in density. Although density was more sensitive to environmental variation than was pattern, we did not find the clumped pattern widely described for P. ponderosa forests to be universal across ecosystems. Results suggest that (i) multivariate combinations of soil and climatic properties influenced historical forest structure, (ii) as much variation exists in reference conditions within the study landscape as between P. ponderosa regions, (iii) ecosystem classification is a useful framework for quantifying spatial variation in reference conditions, and (iv) determining spatial variation in reference conditions can assist resource managers in prioritizing areas for management and in developing ecosystem-specific management strategies within landscapes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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