MétaCan
Menu
← Back to cohort
Record W6967269241 · doi:10.5063/f1hd7t18

Aeshna canadensis (Canada darner) dragonfly phenology and migration in Minnesota and Canada, 2017-2019

2020· dataset· en· W6967269241 on OpenAlexaboutno aff

Bibliographic record

VenueCalifornia Digital Library · 2020
Typedataset
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsDragonflyPhenologyNational parkShoreOdonataHabitat

Abstract

fetched live from OpenAlex

The purpose of this dataset was to investigate whether Aeshna canadensis (Canada darner) is a migratory dragonfly species by combining phenological observations with a dragonfly wing stable hydrogen isotope isoscape analysis. Phenological data were collected from three study ponds (Washington County; 45.17°N, -92.84°W) in the St. Croix River Valley in central Minnesota. We collected emerging teneral dragonflies from May - October and observed adult flight behavior. We also collected adult dragonflies on the wing. Additionally, adult Canada darners were collected in northern Minnesota (Itasca State Park and the western shore of Lake Superior) and we accessed specimens in Canadian museum collections (Royal Ontario Museum and the Canadian National Collection of Insects and Arachnids). We conducted probabilistic assignment to natal origins by measuring stable hydrogen isotope values of wing tissue (δ2Hw) of collected dragonflies. These analyses were done at the Center for Stable Isotopes, University of New Mexico and at the Laboratory for Stable Isotope Science – Advanced Facility for Avian Research.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.094
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.004
GPT teacher head0.162
Teacher spread0.158 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCalifornia Digital Library→Same topicIsotope Analysis in Ecology→French-language works237,207→