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Record W3214404728 · doi:10.1656/045.028.0403

Photographic Monitoring of Blooming of Critical Salt Marsh Nectar Sources by Citizen Scientists

2021· article· en· W3214404728 on OpenAlexaffabout
Bridget A. Rusk, Liette Cormier, Serge Jolicoeur, Gail L. Chmura

Bibliographic record

VenueNortheastern Naturalist · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversité de MonctonMcGill UniversityHEC Montréal
Fundersnot available
KeywordsNectarBayButterflySalt marshPollinatorEcologyPhenologyMarshOrnamental plantShadingGeographyBiologyPollenPollinationArchaeologyWetlandComputer science

Abstract

fetched live from OpenAlex

Climate warming is likely to cause differential shifts in the phenology of pollinators and nectar sources. Detection of these shifts requires careful observation of emergence and peak populations of both the animals and plants involved. On salt marshes of Canada's Chaleur Bay, the potential for asynchronous appearance of the adults of the endangered butterfly Coenonympha nipisiquit (Maritime Ringlet) and its primary nectar sources has become a concern. We used citizen scientists and simple equipment to collect field observations of blooming of key nectar sources: Lysimachia maritima (= Glaux maritima) (Sea Milkwort), Limonium carolinianum (Sea Lavender, Carolina Sea Lavender, or American Thrift), and Solidago sempervirens (Seaside Goldenrod). These species have distinctly different flowering architectures that present varied challenges to observations of initiation of blooming and peak blossoming; therefore, our results have value to a diversity of environments. We show how techniques of remote sensing can be applied to analyze photographs collected by citizen scientists, thus providing records of peak blooming and eliminating observer bias. The success of photographic monitoring depends upon floral architecture and simple shading to prevent oversaturation of sunlight.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.224

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 science0.0000.000
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 head0.026
GPT teacher head0.234
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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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