MODELISATION DES NICHES CLIMATIQUES DE DEUX ESPECES D’OISEAUX GIBIERS D’EAU MENACES AU SUD DU BENIN
Bibliographic record
Abstract
Birds of wetlands in southern of Benin are very threatened because of the human pressures. Some of them are very hunting like birds game for therapeutic, food and practices by local populations. More appraisals are:Porphyrio alleni and Porphyrio porphyrio. To predict the habitats of these species for the effects of climatic changes which are announced and to avoid a biodiversity decline in Benin, a study on the modeling of climatic niches was doing on these 2 species in Ramsar sites 1017 and 1018. The bird’s census method used is based on point’s count of 15 minutes. The various tools and methods for analyses used related to coordinate points of the presence sites of species and modeling of their niche under the climatic models CCCMA (Canadian Centre for Climate Modelling and Analysis) et CSIRO (Commonwealth Scientific and Industrial Research Organisation) using MAXENT 3.3.2. program. Results showed that among the variables selected with the prediction of climatic models of the two species of water birds, the distance water (Diswater) and altitude (alt) appeared like the environmental variables having more contributed to the prediction of the models. On average 74.32 % for the variable outdistance compared to 12.94 % and river for altitude. Projection under CCCMA and CSIRO in 2050 showed that on the scale of Benin, all the species will be in the future confined in habitats of survival. This work contributes to constitute data base for the habitats prediction of these 2 birds’ species and can be exploited for installation and conservation by managers of protected areas in Benin.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".