Effect of precommercial thinning followed by a fertilization regime on branch diameter in coastal United States Douglas-fir plantations
Bibliographic record
Abstract
The effect of precommercial thinning in 6- to 13-year-old Douglas-fir ( Pseudostuga menziesii (Mirb.) Franco var. menziesii ) plantations with and without fertilization with 224 kg·ha–1nitrogen (N) as urea on the mean diameter of the largest limb at breast height (DLLBH) was modeled. DLLBH is a simple, nondestructive field measurement related to log knot indices used to measure log quality in product recovery studies. Model [1] succeeded in predicting mean DLLBH (RMSE = 2.80 and radj2= 0.84) using only site, initial stocking, and treatment variables. Model [2], which used only mean tree variables, improved on model [1] and was simpler. However, model [3], which used a combination of both groups of variables, produced the best model. Model [4] successfully predicted mean DLLBH using variables that can be measured with light detection and ranging (LIDAR), a high-resolution remote sensing technology. Since the age when the live crown receded above breast height is an important variable in some of the models, model [5] was developed to predict when crown recession above breast height occurs. This study found that mean DLLBH of Douglas-fir plantations can be estimated using variables obtained from stand-level growth models or remote sensing, providing a quality indicator that can be easily measured and verified in the field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".