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Record W2954842468

Are we providing the preferred floral resources for bees in our neighborhoods? Relationships between small scale vegetation metrics and pollinator visitation in SE Portland

2019· article· en· W2954842468 on OpenAlexaboutno aff
Hailey Wallace, Marion Dresner

Bibliographic record

VenuePDXScholar (Portland State University) · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsPollinatorVegetation (pathology)Scale (ratio)EcologyGeographyCitizen scienceUrban ecologyBiologyPollinationPollenCartographyHabitatMedicine
DOInot available

Abstract

fetched live from OpenAlex

Due to the threat of losing our pollinators, there are many conservation actions such as "pollinator friendly" areas being constructed in cities around the globe, because of this there is a need for a greater understanding of the relationship between bees, and floral resources at a local landscape level. I assessed the relationship between blossom density, inflorescence type, cover, frequency, density and numbers of bees observed at three different "pollinator friendly" areas in South East Portland. This project utilized community science members to gather observational monitoring data at Johnson Creek Commons Rain Garden, SE Yukon Bioswales and Beyer Court Rain Garden in Lents, Oregon. I hypothesize several significant findings from my research, such as the relationship between small scale vegetation metrics and floral visitor activity, and a relationship between diversity and richness of sites and morpho-species groupings. The results of this study will be utilized in developing a deeper understanding of the relationship between pollinators and floral resources at a local landscape level, and in addition will provide recommendations for floral resources used in "pollinator friendly" projects in conservation areas in South East Portland.

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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.090
GPT teacher head0.223
Teacher spread0.133 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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