Monthly leaf area index estimates from point-in-time measurements and needle phenology for <i>Pinus taeda</i>
Bibliographic record
Abstract
Leaf area index (LAI) of loblolly pine (Pinus taeda L.) trees of the southern United States varies almost twofold interannually; loblolly pine, essentially, carries two foliage cohorts at peak LAI (September) and one at minimum (MarchApril). Herein, we present an approach that may be site invariant to estimate monthly LAI for loblolly pine using point-in-time measurements from a LI-COR LAI-2000 plant canopy analyzer (PCA). Our analyses used needle accretion and abscission data from monthly needle counts and destructive harvest data from a replicated 2 × 2 factorial experiment of water and nutrition amendments. No significant treatment effects on relative needle accretion or abscission were observed. Cohort (interannual) differences in needle accretion were found but appeared trivial. Cohort year had variable effects on needle abscission. Abscission of current-year foliage began in July and continued through November of the third year; however, only 7%9% remained 23 months following bud initiation. A treatment-invariable regression of PCA measurements on cohort foliage biomass (r2 [Formula: see text] 0.98) was used to estimate annual cohort LAI. We derived monthly estimates of LAI from cohort accretion and abscission and cohort LAI. Monthly estimates of LAI for loblolly pine, using point-in-time measurements from the PCA, appear possible, although further testing is required.
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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 | 0.001 |
| 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) | 0.001 | 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".