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Radiotelemetry reveals differences in individual movement patterns between outbreak and non‐outbreak Mormon cricket populations

2005· article· en· W2062534865 on OpenAlexaff
Patrick D. Lorch, Gregory A. Sword, Darryl Gwynne, Gerald L. Anderson

Bibliographic record

VenueEcological Entomology · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsUniversity of Toronto
FundersU.S. Department of Agriculture
KeywordsOutbreakBiologyCricketEcologyPEST analysisMovement (music)GeographyZoologyDemographyVirology

Abstract

fetched live from OpenAlex

Abstract. 1. Outbreaks of insect pest populations are common and can have devastating effects on natural communities and on agriculture. Little is known about the causes of these outbreaks or the causes of en masse migrations during outbreaks. 2. Flightless Mormon crickets ( Anabrus simplex ) were the focus of this study. They are a katydid species that forms large, dense, mobile groups (migratory bands) during outbreak periods, eating vegetation in their path. 3. Radiotelemetric methods were used to measure differences in movement rate and directionality in outbreak and non‐outbreak populations, testing the hypothesis that these populations differ in their travel rate and consistency of direction. 4. Daily individual movement in outbreak populations differs substantially from non‐outbreak populations that are at much lower density. In addition to large differences in distances travelled (1.6 km as compared with 1 m) and rates of travel, there is evidence for collective movement among individual Mormon crickets travelling in migratory bands. 5. These data suggest that the direction of group movement may be influenced by local environmental conditions such as wind direction and movement of nearby band members. This work forms the basis for ongoing work testing hypotheses about mass migrations in outbreak populations.

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 score0.007
Threshold uncertainty score0.721

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 integrity0.0010.000
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 head0.037
GPT teacher head0.294
Teacher spread0.257 · 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

Citations69
Published2005
Admission routes1
Has abstractyes

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