Models to predict whole-disk specific gravity and moisture content in planted longleaf pine from cutover and old field sites
Bibliographic record
Abstract
Efforts to restore longleaf pine across the southeast United States have occurred on two distinct site types: cutover forests and old agricultural fields. We measured wood and bark physical properties of unthinned planted longleaf pine from 16 stands across Georgia, ages 12 to 25, with eight stands sampled from each site type. Three-hundred and twenty trees were felled and 3572 disks collected from within the trees. Wood and bark specific gravity (SG), moisture content (MC), and proportion of bark were measured. Non-linear mixed effects models were developed to predict the variation in wood and bark SG with respect to relative height, age, and site type. Cutover sites had higher whole-tree wood SG (0.504 vs 0.455) and bark SG (0.374 vs 0.347) than old agricultural fields. The models explained 50% and 37% of the variability in wood and bark SG, respectively. Moisture content models were fitted as a function of SG for wood (R2 = 0.87) and bark (R2 = 0.71). Bark thickness, dry mass, and green volume were higher for cutover forest sites. Trees sampled included both defect-free and defect-containing trees; however, no significant differences in the wood physical properties were found. These results provide important information for the utilization of plantation longleaf pine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".