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Record W2896661703 · doi:10.3958/059.043.0302

Phenology and Abundance of Northern Tamarisk Beetle, <i>Diorhabda carinulata</i> Affecting Defoliation of <i>Tamarix</i>

2018· article· en· W2896661703 on OpenAlexfundno aff
Levi R. Jamison, Matthew J. Johnson, Dan W. Bean, Charles van Riper

Bibliographic record

VenueSouthwestern Entomologist · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationU.S. Geological SurveyMcMaster University
KeywordsBiologyPhenologyVoltinismDiapauseAbundance (ecology)TamarixEcologyLarvaAgronomy

Abstract

fetched live from OpenAlex

Timing and spatial dynamics of tamarisk (Tamarix spp. L.) defoliation by the biological control agent Diorhabda carinulata (Desbrochers) were evaluated. Relative abundance of D. carinulata and the phenology of tamarisk along the San Juan and Colorado rivers were recorded in 2011–2012. D. carinulata began reproducing in the spring when temperatures were >15°C. Variation in spring temperature-rise affected the timing of development of larvae of the first summer generation and initial defoliation of tamarisk at each site. Shortening day lengths in mid- to late-summer cued D. carinulata to enter reproductive diapause resulting in cessation of defoliation. The critical day length for inducing reproductive diapause was 33–47 minutes shorter than that of populations of D. carinulata released into North America in 2001. Variation in spring temperature-rise combined with timing of shortening day length resulted in differences in D. carinulata voltinism per site. During the active season, larvae were less likely to establish in areas where defoliation was >70%. Lack of reestablishment of larvae led to temporary loss of D. carinulata from the locations and allowed tamarisks to sprout new canopies. Defoliation of tamarisk was dictated by environmental cues and abundance of D. carinulata, and in turn large amounts of defoliation negatively affected abundance of D. carinulata.

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 score0.031
Threshold uncertainty score0.604

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.227
Teacher spread0.218 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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