Modelling the distribution of a potential invasive tropical fern, <i>Cyclosorus afer </i>in Nigeria
Bibliographic record
Abstract
Abstract In this study, we predicted the distribution of Cyclosorus afer in Nigeria using the Maximum Entropy (Maxent) technique. We used 95 occurrence points in one State to extrapolate its spread in other States in Nigeria. Three sites of sizes 500 × 500 m2 separated by minimum distance of 1,000 m2 were sampled in the study area. Seven bioclimatic and elevation variables were selected for the model. Maxent model was run using standard settings with 70% of the occurrence data as training and remaining 30% as test data. The result showed that Maxent performed better than random prediction due to the area under curve for receiver operating characteristics of training (0.990) and test data (0.987) closer to 1. The sensitivity of the Maxent model for occurrence data of C. afer was found to be 100%. The model predicted higher probability of occurrence covering an area of 26019.11 km2 in 4 States of Nigeria. Jackknife evaluation of the model revealed that the environmental predictors of C. afer in Nigeria are annual mean temperature, mean temperature of driest quarter, precipitation seasonality, Precipitation of driest quarter and precipitation of coldest quarter. These variables all showed higher gain and contributions to the model.
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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 | 0.001 | 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".