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Record W4394183144 · doi:10.6084/m9.figshare.1565048

Insect Diversity and Abundance Determined by Pan Trap Sampling Method at York University

2015· dataset· en· W4394183144 on OpenAlexaboutno aff
Nicole Gallagher

Bibliographic record

VenueFigshare · 2015
Typedataset
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTrap (plumbing)Abundance (ecology)Sampling (signal processing)Diversity (politics)Environmental scienceGeographyEcologyBiologyEngineeringSociologyTelecommunicationsAnthropologyEnvironmental engineering

Abstract

fetched live from OpenAlex

The related data was collected on Tuesday, September 29th 2015, between the times of 3:00 to 4:30 pm, in collaboration with Bonnie Duong, Markian Plawiuk, Muhammad Akram and Daniel Germani . There was a moderate rain, as well as cloud cover, throughout the duration of the experiment. Nine pan traps each were placed in the Danby grassland and Danby Woodlot at York University, Keele campus, Toronto, Ontario. In each location, i.e. woodlot or grassland, nine bowls of alternating colours of blue, yellow and white, were place in a single file linear formation approximately 2 metres apart, for a total of 18m. The bottoms of the bowls were covered with about a centimetre of water to deter the insects from escaping. The traps were then left undisturbed for approximately one hour. Upon returning, the contents of the bowls were emptied into the sieve to better observe the individuals and the RTUs.

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)0.001
metaresearch head score (Gemma)0.003
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0320.017

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 head0.114
GPT teacher head0.287
Teacher spread0.173 · 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
Published2015
Admission routes1
Has abstractyes

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