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Record W1992501598 · doi:10.5558/tfc2015-029

Determining the glyphosate tolerance of eastern white cedar: First year post-treatment results

2015· article· en· W1992501598 on OpenAlexafffundvenue
Thomas L. Noland, Rongzhou Man, Michael Irvine

Bibliographic record

VenueThe Forestry Chronicle · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsOntario Forest Research InstituteMinistry of Natural Resources and Forestry
FundersOntario Ministry of Natural Resources and ForestryMinistry of Natural Resources
KeywordsGlyphosateHerbaceous plantBiologyCompetition (biology)Weed controlHorticultureAgronomyAnimal scienceBotanyEcology

Abstract

fetched live from OpenAlex

Little is known about the silvicultural traits of eastern white cedar (Thuja occidentalis L.), and especially the tolerance of seedlings to herbicides. A study was established to determine the sensitivity of cedar seedlings to timing and concentration of glyphosate applications. Glyphosate was applied to seedlings at three concentrations (1.04, 2.07, and 4.14 acid equivalent (ae) kg ha -1 ) at three times (July 28, August 10 and August 31) and their survival and growth was compared with that of seedlings in three control treatments (competition free, always competition, and competition free after an August 10 application of 2.07 kg ha -1 glyphosate) for a total of 12 treatments. Damage to foliage increased (r 2 = 0.99) and first-year volume growth decreased (r 2 = 0.91) with increasing glyphosate concentration. Glyphosate applied at 4.14 kg ha -1 killed 9% of the seedlings; even the lowest concentration of glyphosate (1.04 kg ha -1 ) reduced first-year volume growth more than continuous herbaceous competition. Application timing did not affect the amount of foliar damage or the growth of seedlings. However, in the winter following the treatments, snow damage was greater in the weed-free control seedlings than in all other treatments.

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 score0.518
Threshold uncertainty score0.285

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)0.0000.000

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 head0.029
GPT teacher head0.234
Teacher spread0.206 · 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

Citations4
Published2015
Admission routes3
Has abstractyes

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