Effects of Season and Successional Stage on Leaf Area Index and Spectral Vegetation Indices in Three Mesoamerican Tropical Dry Forests<sup>1</sup>
Bibliographic record
Abstract
ABSTRACT We compared plant area index (PAI) and canopy openness for different successional stages in three tropical dry forest sites: Chamela, Mexico; Santa Rosa, Costa Rica; and Palo Verde, Costa Rica, in the wet and dry seasons. We also compared leaf area index (LAI) for the Costa Rican sites during the wet and dry seasons. In addition, we examined differences in canopy structure to ascertain the most influential factors on PAI/LAI. Subsequently, we explored relationships between spectral vegetation indices derived from Landsat 7 ETM+ satellite imagery and PAI/LAI to create maps of PAI/LAI for the wet season for the three sites. Specific forest structure characteristics with the greatest influence on PAI/LAI varied among the sites and were linked to climatic differences. The differences in PAI/LAI and canopy openness among the sites were explained by both the past land‐use history and forest management practices. For all sites, the best‐fit regression model between the spectral vegetation indices and PAI/LAI was a Lorentzian Cumulative Function. Overall, this study aimed to further research linkages between PAI/LAI and remotely sensed data while exploring unique challenges posed by this ecosystem.
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 | 0.000 | 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".