A comparison of alternative methods for estimating the self-thinning boundary line
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Bibliographic record
Abstract
The fundamental validity of the self-thinning "law" has been debated over the last three decades. A long-standing concern centers on how to objectively select data points for fitting the self-thinning line and the most appropriate regression method for estimating the two coefficients. Using data from an even-aged Pinus strobus L. stand as an example, we show that quantile regression (QR), deterministic frontier function (DFF), and stochastic frontier function (SFF) methods have the potential to produce an upper limiting boundary line above all plots for the maximum sizedensity relationship, without subjectively selecting a subset of data points based on predefined criteria. On the other hand, ordinary least squares (OLS), corrected ordinary least squares (COLS), and reduced major axis (RMA) methods are sensitive to the data selected for model fitting and may produce self-thinning lines with inappropriate slopes. However, statistical inference is very difficult with the DFF and QR methods. Although SFF produces a self-thinning line lower than the upper limiting boundary line because of the nature of the method, it can easily produce the statistics for inference on the model coefficients, given that there are no significant departures from underlying distributional assumptions.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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