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Record W4403682943 · doi:10.1080/14888386.2024.2410013

Rapid declines in freshwater gastropods in Pune city, India

2024· article· en· W4403682943 on OpenAlexaff
Akash Bagade, Mihir R. Kulkarni, Saurabh Khandare, Abhay Khandagle, N. A. Aravind, Yugandhar Satish Shinde, S. Padhye

Bibliographic record

VenueBiodiversity · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGeographyEcologyGastropodaFisheryEnvironmental protectionBiology

Abstract

fetched live from OpenAlex

We studied freshwater gastropod fauna in a heavily urbanized region in tropical India, across a range of habitat types. A total of 16 species were found from a two-year survey, which is a decline of 27% compared to studies completed in the 1970s. Planorbidae was the most species rich family in the collection, with five species. Racesina luteola (Lamarck, 1822) was the most commonly occurring species in the collection. Three non-native species, including Pomacea diffusa (Blume, 1957), are also reported. Habitat type influenced species occurrences, although lentic and lotic habitats were largely similar in their fauna. Many species reported previously were missing from the current survey, along with an increased incidence of non-native species which were absent from earlier reports. This decline in species richness, particularly of native species, alongside an increased incidence and spread of non-native species in the region, highlights the impact of urbanization on the freshwater gastropod community.

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 score0.040
Threshold uncertainty score0.080

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 communication0.0010.001
Open science0.0010.001
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 head0.021
GPT teacher head0.230
Teacher spread0.209 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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