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Record W2341362072 · doi:10.14288/1.0042685

Effect of soil amendments (mats and hydroseeding) on establishment success of four native grassland species

2014· article· en· W2341362072 on OpenAlexaboutno aff
M. Phillips, Wendy C. Gardner, L. Fraser, J. Karakatsoulis

Bibliographic record

VenuecIRcle (University of British Columbia) · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
Fundersnot available
KeywordsGrasslandEnvironmental scienceAgroforestryAgronomyGeographyBiology

Abstract

fetched live from OpenAlex

Restoration of grassland ecosystems is challenging due to moisture limitation, competition from nonnative invasive plants, and difficulty in establishing native grassland species. A manipulative experiment was designed to study the establishment for four native grassland species using combinations of two soil amendments in the lower grasslands of Kamloops, BC. The treatments included the use of a straw germination mat with seed placed either on top or underneath, hydroseeding, a combination of the straw matting with the hydro-seeding either below or on top, seeding without any soil amendment and a control with no seeding or amendment. Treatments were each replicated 6 times in a randomized complete block design. Approximately 150 seeds of each bluebunch wheatgrass (Pseudoroegneria spicata), rough fescue (Festuca campestris), brown-eyed Susan (Gaillardia aristata) and long-leaved daisy (Erigeron filifolius) were broadcast seeded per plot. Soil moisture and temperature, and cover of functional groups on each treatment were recorded and statistically analyzed. The highest cover by functional group was undesirables, none of which were seeded species, indicating the presence of plant propagules at this site. It was found that mat treatments, with seeding below, resulted in the greatest cover of seeded species and the most desirable ratio of grass, forbs and undesirable species.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.969

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 head0.010
GPT teacher head0.179
Teacher spread0.169 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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