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Record W3089972257 · doi:10.5539/enrr.v10n3p16

In Niger, the Expected Futur Climate Will Provide Better Conditions than the Current One to Diospyros Mespiliformis Hochst. ex A.DC. Rich.

2020· article· en· W3089972257 on OpenAlexvenueno aff
Laouali Abdou, Maman Maârouhi Inoussa, Josiane Seghieri, Ali Mahamane

Bibliographic record

VenueEnvironment and Natural Resources Research · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeGeographyCurrent (fluid)Physical geographyPopulationEnvironmental scienceEcologyAgroforestryBiologyGeology

Abstract

fetched live from OpenAlex

The human use of plant resources and land to face increasing population needs in Africa to the regression or even the disappearance of some useful multi-purpose species such as Diospyros mespiliformis Hochst. Ex A. Rich. Increasing climatic variability is an additional threat for these species. The present study aims to identify the areas that are potentially favorable to D. mespiliformis conservation or regeneration in Niger and to analyze the impact of the current climate change. Thus to assess the D. mespiliformis distribution areas, the geographic coordinates of D. mespiliformis, the bioclimatic data, the soil and vegetation cover were collected and used to modeling based on the principle of maximum entropy (MaxEnt). The soil cover, annual cumulated precipitations and the average temperature are the most determining variables. This study also shows that the ecological niche of D. mespiliformis is located in the Central and Eastern bioclimates, within which almost 3% of the surface is very favorable under the current climate conditions and may reach 3. 94 % under 2050 ones after. These results indicate that the climate change expected in Niger is expected to be more favorable to the studied species than the current climate conditions. This represents an opportunity for its domestication.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.276
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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