Leaf area duration in natural range and exotic Pinus taeda
Bibliographic record
Abstract
Exotic Pinus taeda L. plantations may be more productive than native ones. Several hypotheses may explain this difference; however, process models with a light-interception-driving variable cannot test these hypotheses without foliage display first being quantified in native and exotic trees. We quantified leaf area duration in North Carolina, USA (natural), and Gobernador Virasoro, Argentina (exotic), with no additional nutrients and optimum fertilizer treatments. More (60%–100%) foliage was displayed but for a shorter (∼86 fewer days) time per fascicle in the exotics than in the naturals. Study inference was limited, with only one native and one exotic site. However, while the sites were markedly different in soils, climate, resource availability, and genetics, and we observed significant differences in fascicle display and longevity, most fascicles at both sites survived two growing seasons: the one in which they were produced and the subsequent one. This robust finding indicates it would be reasonable to use two growing seasons for fascicle longevity in process modeling to test hypotheses explaining growth differences in native and exotic loblolly. Fertilization had no effect on any exotic tree parameter, but it increased natural tree fascicle number (24%) and length (30%).
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".