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Record W3015530857 · doi:10.3390/su12072936

Living under a Fluctuating Climate and a Drying Congo Basin

2020· article· en· W3015530857 on OpenAlexfundno aff
Dénis Sonwa, Mfochivé Oumarou Farikou, M. Gapia, Fiyo Losembe Félix

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
FundersDirektoratet for UtviklingssamarbeidConsortium of International Agricultural Research CentersDepartment for International DevelopmentInternational Development Research Centre
KeywordsDeforestation (computer science)LivelihoodStructural basinClimate changeBiodiversityGeographyEnvironmental degradationWater cycleAgroforestryEnvironmental scienceEnvironmental protectionEcologyAgricultureGeologyBiology

Abstract

fetched live from OpenAlex

Humid conditions and equatorial forest in the Congo Basin have allowed for the maintenance of significant biodiversity and carbon stock. The ecological services and products of this forest are of high importance, particularly for smallholders living in forest landscapes and watersheds. Unfortunately, in addition to deforestation and forest degradation, climate change/variability are impacting this region, including both forests and populations. We developed three case studies based on field observations in Cameroon, the Central African Republic, and the Democratic Republic of Congo, as well as information from the literature. Our key findings are: (1) the forest-related water cycle of the Congo Basin is not stable, and is gradually changing; (2) climate change is impacting the water cycle of the basin; and, (3) the slow modification of the water cycle is affecting livelihoods in the Congo Basin. Developmental and environmental actions in the Congo Basin need to properly consider the slight modification of this water cycle in watersheds that affect products and services from the forest.

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 score0.013
Threshold uncertainty score0.356

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 science0.0000.001
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 head0.010
GPT teacher head0.233
Teacher spread0.223 · 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

Citations27
Published2020
Admission routes1
Has abstractyes

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