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Mapping invasive alien plants through citizen science: shortlisting species of concern for the Nilgiris

2023· article· en· W4389005594 on OpenAlexfundno aff
Shiny Mariam Rehel, R. S. Reshnuraj, Samuel Thomas, Milind Bunyan, Anita Varghese, Ankila J. Hiremath

Bibliographic record

VenueJournal of Threatened Taxa · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
FundersDepartment for International DevelopmentInternational Development Research CentreNational Geographic Society
KeywordsAlienInvasive speciesAlien speciesHabitatBiodiversityIntroduced speciesCitizen sciencePlant speciesGeographyEcologyDistribution (mathematics)Native plantOrnamental plantAgroforestryBiologyPolitical scienceBotany

Abstract

fetched live from OpenAlex

Species introduced from elsewhere are known as alien species. They may be introduced as crop plants or ornamental plants, or for timber. A small proportion of introduced species can become invasive thereby spreading at the cost of native species and habitats, negatively affecting biodiversity, food security, and human wellbeing. Despite the growing recognition of the threat of invasive alien species, we still lack information about the distribution and abundance of species widely accepted to be invasive. To address this information gap regarding invasive alien species distributions, we initiated a pilot citizen science effort to create an atlas of invasive plants in the Moyar-Bhavani landscape of the Nilgiri District. We aimed, through this pilot effort, to develop and test user-friendly mapping protocols and develop an interface for citizen scientists to use. Ultimately, we hope to create a model that can be scaled up to large conservation landscapes, such as the Western Ghats, the central Indian Highlands, and the Himalaya.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.119
GPT teacher head0.309
Teacher spread0.190 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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