Contribution of lianas to plant area index and canopy structure in a Panamanian forest
Bibliographic record
Abstract
Lianas are an important component of tropical forests, where they reduce tree growth, fecundity, and survival. Competition for light from lianas may be intense; however, the amount of light that lianas intercept is poorly understood. We used a large-scale liana-removal experiment to quantify light interception by lianas in a Panamanian secondary forest. We measured the change in plant area index (PAI) and forest structure before and after cutting lianas (for 4 yr) in eight 80 m × 80 m plots and eight control plots (16 plots total). We used ground-based LiDAR to measure the 3-dimensional canopy structure before cutting lianas, and then annually for 2 yr afterwards. Six weeks after cutting lianas, mean plot PAI was 20% higher in control vs. liana removal plots. One yr after cutting lianas, mean plot PAI was ~17% higher in control plots. The differences between treatments diminished significantly 2 yr after liana cutting and, after 4 yr, trees had fully compensated for liana removal. Ground-based LiDAR revealed that lianas attenuated light in the upper- and middle-forest canopy layers, and not only in the upper canopy as was previously suspected. Thus, lianas compete with trees by intercepting light in the upper- and mid-canopy of this forest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".