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Record W2205439296 · doi:10.48044/jauf.2000.023

Vegetation Management Along Transmission Utility Lines in the United States and Canada

2000· article· en· W2205439296 on OpenAlexaboutno aff
Joseph A. Sulak, J. James Kielbaso

Bibliographic record

VenueArboriculture & Urban Forestry · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotany and Plant Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVegetation (pathology)Electric power transmissionGeographyTransmission (telecommunications)BusinessTelecommunicationsEngineeringMedicineElectrical engineering

Abstract

fetched live from OpenAlex

Abstract A survey investigating vegetation control methods along transmission rights-of-way was sent to 220 Utility Arborist Association companies. The survey contained questions regarding right-of-way characteristics, control methods used, total dollars spent on vegetation management, and priorities of the vegetation management program. The ROW area reported represented over 48% of all the investor-owned ROWs over 39 Kv in service throughout the United States. More than 75% of the respondents reported using herbicides on their rights-of-way. However, acres treated mechanically outnumbered those treated chemically by a margin of 2.7:1. Garlon 3A and Garlon 4 topped all herbicides, with a combined 220,574 projected gal (834,961 L) of the estimated 549,869 gal (2,081,474 L) of herbicide applied to transmission rights-of-way in 1995. It appears that quite low levels of active ingredients are being applied per acre. Basal, high-volume foliar, and low-volume foliar with a backpack or handgun applications accounted for approximately 75% of the acres of transmission ROWs treated with herbicides.

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 score0.591
Threshold uncertainty score0.827

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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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

Citations3
Published2000
Admission routes1
Has abstractyes

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