Modeling genetic effects on growth of diverse provenances and families of loblolly pine across optimum and deficient nutrient regimes
Bibliographic record
Abstract
Optimal deployment of improved loblolly pine (Pinus taeda L.) planting stock in the southeastern United States requires knowing how diverse seed sources and families perform over time across the wide range of sites used for plantations. This study tests if the relative growth performance of provenances and families is the same at the individual-tree and stand levels for family block plantings and determines what type of adjustment may be required to account for genetic differences when modeling growth and yield. Ten open-pollinated families from two very different provenances, Atlantic Coastal Plain and “Lost Pines” Texas, were grown in single-family block plots to test for growth differences between provenances and among families under severely deficient and optimal nutrition regimes on a nutrient-deficient, dry site. The three-parameter Chapman–Richards function was fit to plot means over time by provenance, family, and nutrition treatments. Models with provenance- or family-specific parameters of the Chapman–Richards function were tested for significant improvement over global parameters. At age 14 years, family, provenance, and nutrition treatments all significantly affected individual-tree growth traits of height, diameter, and volume. Significant nutrition by provenance interactions were found for stand-level traits of basal area per hectare and volume per hectare. Family differences were also significant for these traits. Provenance- or family-specific asymptotic parameters accounted for differences in growth over time. Several traits required the use of local asymptotic and rate parameters in the fertilized treatment only. For modeling growth, a multiplier would be sufficient to account for genetic effects on the majority of traits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".