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Record W4390060648 · doi:10.1080/11956860.2023.2293259

Grazing and right-of-way affect native rangeland 12 years after pipeline construction in southern Alberta

2023· article· en· W4390060648 on OpenAlexaffvenueabout
D. Kelly Ostermann, Amalesh Dhar, M. Anne Naeth

Bibliographic record

VenueEcoscience · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRangelandGrazingRevegetationVegetation (pathology)Land reclamationGrasslandNative plantDisturbance (geology)Environmental scienceDiggingEcologyPlant communityGeographyLitterAgroforestryIntroduced speciesEcological successionBiology

Abstract

fetched live from OpenAlex

Over the past 100 years, large areas of native grasslands have been lost due to human activities and natural disturbances. Construction of pipelines for oil and gas transportation continues to pose significant challenges to grassland ecosystems. Thus, reclamation of disturbed native grasslands is critical for their existence in North America and around the world. This study investigated long-term (12 years) effects of grazing and right-of-way (RoW) treatments on revegetation of native rangeland on two pipelines in southeastern Alberta, Canada. Grazing and RoW treatments influenced soil and vegetation parameters; and plant species group responded differently at Milo and Porcupine Hills. Grazing was associated with significantly greater bare ground and decreased litter at both sites and increased vegetation cover at Porcupine Hills. At Milo grass density and biovolume increased as RoW disturbance increased, but not at Porcupine Hills. Trenching increased rhizomatous grasses and all RoW disturbances reduced tufted grasses. Vegetation was dissimilar on the RoW from undisturbed prairie with intermediate levels of disturbance (work, storage) having greater plant species diversity, whereas grazing had no effect. This study suggests 12 years may not be long enough for restoration of native rangelands after pipeline construction although there was some progress.

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 score0.017
Threshold uncertainty score0.284

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 head0.006
GPT teacher head0.208
Teacher spread0.201 · 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
Published2023
Admission routes3
Has abstractyes

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