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Record W3021922965 · doi:10.3390/su12093829

Streamflow Alterations, Attributions, and Implications in Extended East Rapti Watershed, Central-Southern Nepal

2020· article· en· W3021922965 on OpenAlexfundno aff
Vishnu Prasad Pandey, Dibesh Shrestha, Mina Adhikari, Shristi Shakya

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsStreamflowWatershedEnvironmental sciencePrecipitationClimate changeBiodiversityFlood forecastingWatershed areaDrainage basinHydrology (agriculture)EcologyGeographyGeologyBiology

Abstract

fetched live from OpenAlex

Streamflow alteration and subsequent change in long-term average, seasonality, and extremes (e.g., floods and droughts) may affect water security, which is a major concern in many watersheds across the globe. Both climatic and anthropogenic activities may contribute to such changes. Therefore, this study assesses: (i) Streamflow and precipitation trends to identify streamflow alterations in the Extended East Rapti (EER) watershed in central-southern Nepal; (ii) relationship of the alterations to climatic and anthropogenic sources; and (iii) implications of streamflow changes to the socio-environmental system. The trends in streamflow were analyzed for pre-and post-1990 periods considering the abrupt shift in temperature trend in 1990. Results showed a general decreasing trends in discharge post-1990 in the EER watershed. Human activities have significantly contributed in altering streamflow in the EER. Human-induced streamflow alterations have affected the water availability, food security, river health, aquatic biodiversity, and groundwater abstraction in the EER watershed.

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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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