Reevaluating the self-thinning boundary line for ponderosa pine (<i>Pinus ponderosa</i>) forests
Bibliographic record
Abstract
The self-thinning rule has been used extensively to predict population dynamics under intraspecific and interspecific competition. In forestry, it is an important silvicultural concept for maintaining stand health in the face of climate change and biotic stress, but uncertainty exists because traditional self-thinning limits were set subjectively without regard to site quality. We addressed this by analyzing ponderosa pine (Pinus ponderosa Lawson & C. Lawson) data from 109 research plots measured repeatedly and 59 inventory plots measured once across California. Self-thinning boundaries were fitted to the data with quantile regression and stochastic frontier function (SFF) techniques with and without site index (SI) as a covariate. The models from both methods fitted the data well with either research plots or all plots. Slopes for size-density trajectories were –0.45 with the 0.99 quantile and –0.47 for SFF. Maximum stand density indices (SDI) were 1250 trees per hectare (TPH) with the 0.99 quantile and 1050–1060 TPH with SFF. Mortality occurred when site occupancy from SFF reached 0.75, suggesting a zone of imminent mortality. Curvilinear trends in maximum SDI across SI for both methods indicate that self-thinning varies with site quality. Any management regimes that increase site quality and productivity will increase the self-thinning boundary.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".