Ecological niche modeling on the effect of climatic change and conservation of <i>Ternstroemia lineata</i> DC. (Ternstroemiaceae) in Mesoamerica
Bibliographic record
Abstract
A bioclimatic modeling of the tree Ternstroemia lineata DC. (Ternstroemiaceae) was undertaken. The genus itself is considered as diagnostic or characteristic of the Mexican cloud forest. Ternstroemia lineata has the broadest distribution of all species in this family in Mexico and northern Central America. This species consists of two subspecies, T. lineata subsp. lineata and T. lineata subsp. chalicophila (Loes.) B.M. Barthol. Ecological niche models for both subspecies were generated using the genetic algorithm for rule-set production method. The ecological models for the years 2020, 2050, and 2080 were obtained under the effects of global climatic change considering two emission scenarios (A2a and B2a). When climatic change values were introduced, the ecological niche representation for both subspecies contracted in such a way that they became almost lost throughout their entire geographical range. In both cases, predictions for all years in both scenarios contracted more than 90%. This species may not be able to adapt to modifications caused by climatic change to future conditions, so it is at risk of extinction in the immediate future.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".