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Record W175510860 · doi:10.22621/cfn.v120i1.239

Responses of Syrphids, Elaterids and Bees to Single-tree Selection Harvesting in Algonquin Provincial Park, Ontario

2006· article· en· W175510860 on OpenAlexaffvenueabout
Erica Nol, Hume Douglas, William J. Crins

Bibliographic record

VenueThe Canadian Field-Naturalist · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsMinistry of Natural Resources and ForestryTrent University
Fundersnot available
KeywordsYellow birchUnderstoryEcologyBiologyForestryGeographyApoideaCanopyHymenopteraHardwood

Abstract

fetched live from OpenAlex

The species composition of hoverflies (Syrphidae), click beetles (Elateridae), and bees (Apoidea) was studied to determine whether there was a positive response in these flower-seeking insect groups to gaps in the canopy created through single-tree selection harvesting of Sugar Maple (Acer saccharum) and Yellow Birch (Betula alleghaniensis) in hardwood forests of the Great Lakes-St. Lawrence forest region of Algonquin Provincial Park, Ontario. There were significantly more hoverflies and bees collected in forest stands harvested within the previous five years than in wilderness zone (unharvested at least for 40 years) stands or stands harvested 15-20 years previously (old logged stands). Click beetles, especially Selatosomus pulcher (LeConte), were collected most often in old logged stands. Bees and click beetles were collected significantly later in the season in logged than in wilderness zone stands. Malaise traps resulted in higher capture rates for syrphids than pan traps, and only with these higher capture rates did we detect a significant increase in species richness in recently logged stands over that in wilderness stands. Changes in the numbers and phenology of flower-visiting insects may impact on reproductive success of flowering plants of the forest understory and deserves further study.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.595

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)0.0010.000

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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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

Citations17
Published2006
Admission routes3
Has abstractyes

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