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Record W3146389961

SAVANNA AND FOREST IN WESTERN NIGERIA

2016· article· en· W3146389961 on OpenAlexaboutno aff
R. P. Imoss

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEdaphicTropicsGeographyTropical and subtropical dry broadleaf forestsVegetation (pathology)Tropical savanna climateEcologyTropical vegetationDry seasonPhysical geographyAgroforestrySoil waterEnvironmental scienceEcosystemForestryCartographyBiology
DOInot available

Abstract

fetched live from OpenAlex

The savanna-forest boundary thus appears very clearly, especially on vertical air photographs, and may in consequence be accurately mapped and studied in relation to other features in a number of locations. Such study has the full support of the Special Commission on the Humid Tropics of the International Geographical Union, which is especially concerned with understanding, defining, delimiting, and subdividing the humid tropical environment, and which regards the savanna-forest boundary as one of its most interesting and significant problems. A symposium on the ecology of the boundary was held under its auspices in Venezuela in May 964, and a major symposium to discuss the geography of the humid tropics is planned for I968. Nowhere is the boundary more clearly defined than in Western Nigeria, where the authors already have considerable field experience and where the new infra-red vertical air photographs, taken by Canadian Aero Services during the 1961-2 and 1962-3 dry seasons, provide prints of superb quality especially suitable for the study of vegetation communities in considerable detail.3 The origin of the boundary here, as elsewhere, is a matter of considerable ecological debate. Some authorities stress the importance of burning and cultivation as dominating biotic influences.4 Others assign more importance to climatic controls, such as rainfall, dry season length, and relative humidity, often with an emphasis on the importance of climatic change. Edaphic factors, related to soils, water table levels, and drainage, are also emphasized by some authors.5 More recent work has related major features of the floristic com

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)0.000
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 score0.032
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.206
Teacher spread0.188 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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