MétaCan
Menu
Back to cohort
Record W6981362234

The Effect of Grazing on Leaf Development in Eight Grass Species

2024· article· en· W6981362234 on OpenAlexaboutno aff

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
Fundersnot available
KeywordsExclosureNucleofectionArticular cartilage damagePerformic acidHyporeflexiaWindage
DOInot available

Abstract

fetched live from OpenAlex

Grazing by herbivores affects grass species both morphologically and physiologically. A grazing study was conducted on an irrigated pasture near Outlook, Saskatchewan, Canada during the summers of 1991 and 1992 to determine morphological development of regrowth of reed canarygrass (Phalaris arundinacea L.), slender wheatgrass (Elymus trachycaulus (Link) Gould ex Shinners subsp. trachycaulus), intermediate wheatgrass (Thinopyrum intermedium (Host) Barkw. & D.R. Dewey subsp. intermedium), orchardgrass (Dactylis glomerata L.), meadow bromegrass (Bromus beibersteinii Roem. & Schult.), smooth bromegrass (Bromus inermis Leyss.), tall fescue (Festuca arundinacea (Schreb.) Wimm.) and timothy (Phleum pratense L.) in order to evaluate the suitability of these species for grazing. All eight species were mob-grazed by sheep at a stocking rate of 30 animals ha-1. Physiological stage of leaf development was determined at 7, 14 and 21 d following defoliation. Smooth bromegrass and timothy consistently produced the greatest number of leaves at all regrowth stages. Tall fescue produced the least development of leaves at all time periods. Fifty-five percent of slender wheatgrass tillers and 19% of intermediate wheatgrass tillers were reproductive 21 d after defoliation. Based on leaf growth the grasses were ranked into three groups - meadow and smooth bromegrasses ranked highest, intermediate wheatgrass, reed canarygrass and orchardgrass ranked intermediate and timothy, tall fescue and slender wheatgrass ranked lowest.

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 score0.549
Threshold uncertainty score0.360

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 head0.005
GPT teacher head0.177
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueUKnowledge (University of Kentucky)Same topicRangeland and Wildlife ManagementFrench-language works237,207