Predicting the Distribution of Lygodium circinnatum and Its Environmental Drivers in Mount Rinjani Forest, Indonesia
Bibliographic record
Abstract
Lygodium circinnatum (Burm.f.) Sw. is a well-known species of fern found in Mount Rinjani, used to make a variety of woven handicrafts, and is currently endangered.Therefore, it is essential to understand its distribution in the forest area and potential environmental influences for conservation strategic planning.This study aimed to predict the distribution of and assess the potential environmental influences on L. circinnatum in the forest area of Mount Rinjani.Using the Maxent model, with 10 replications, 500 iterations, and 10,000 background points, the species distribution was created based on environmental factors, i.e., vegetation coverage as indicated by the NDVI (Normalized Difference Vegetation Index) and morphological characteristics (elevation, slope, curvature, aspect, plan curvature, profile curvature, TPI, and TWI).The result showed that the Maxent model was acceptable for defining L. circinnatum distribution with an AUC of 0.82 and the influence of environmental factors on its dispersion.The species preferred to be distributed spatially in the West, East, and Northeast of the forest in Mount Rinjani.Morphological characteristics that played an essential role in influencing the presence of L. circinnatum were slope, elevation, and aspect.Regarding NDVI, the species occurrence was predicted in low to moderate dense vegetation coverage, indicated by low to moderate NDVI values (0.05 -0.35).This study contributes to the understanding of L. circinnatum habitat and provides valuable information for future conservation strategies through providing a 30 m resolution map of species distribution.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".