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Record W2775291077 · doi:10.1016/j.crte.2017.09.004

The changing hydro-ecological dynamics of rivers and deltas of the Western Indian Ocean: Anthropogenic and environmental drivers, local adaptation and policy response

2017· article· en· W2775291077 on OpenAlexfundno aff
Stéphanie Duvail, Olivier Hamerlynck, Paolo Paron, Dominique Hervé, Wanja Dorothy Nyingi, Michele Leone

Bibliographic record

VenueComptes Rendus Géoscience · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
FundersCentre National de la Recherche ScientifiqueMinistry of EnvironmentInternational Development Research CentreCritical Ecosystem Partnership Fund
KeywordsHydropowerEnvironmental scienceFlood mythDeforestation (computer science)Climate changeEcosystemBiodiversityFlooding (psychology)DeltaEcosystem servicesLivelihoodGeographyEcologyOceanographyAgricultureGeology

Abstract

fetched live from OpenAlex

The rivers flowing into the Western Indian Ocean have steep headwater gradients and carry high sediment loads. In combination with strong tides and seasonal rainfall, these rivers create dynamic deltas with biodiversity-rich and productive ecosystems that, through flooding, have sustained indigenous use systems for centuries. However, river catchments are rapidly changing due to deforestation. Hydropower dams also increasingly alter flood characteristics, reduce sediment supply and contribute to coastal erosion. These impacts are compounded by climate change. Altogether, these changes affect the livelihoods of the delta users. Here, based on prior works that we and others have conducted in the region, we analyse the drivers of these hydro-ecological changes. We then provide recommendations for improved dam design and operations to sustain the underlying delta-building processes, the ecosystem values and the needs of the users.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.065
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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
Published2017
Admission routes1
Has abstractyes

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