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Record W1808590598 · doi:10.1016/j.rala.2015.07.002

Seasonal Availability of Cool- and Warm-Season Herbage in the Northern Mixed Prairie

2015· article· en· W1808590598 on OpenAlexafffund
Edward W. Bork, Barry Irving

Bibliographic record

VenueRangelands · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsGrazingAgronomyBiomass (ecology)Growing seasonLitterRangelandGrasslandEnvironmental scienceLivestockBiologyEcology

Abstract

fetched live from OpenAlex

On the Ground • Variability in spatial and temporal patterns of herbage production is common in grasslands and can affect land uses, such as grazing. • Total herbage biomass in northern mixed grass prairie was similar on loamy and sand dune ecologic sites but varied in composition. • Cool-season grasses were uniformly produced throughout the grazing season, whereas warm-season grasses grew rapidly during August. • Litter conservation was important for increasing cool-season grass biomass, whereas warm-season grasses remained independent of litter. • Biomass and composition of herbage in the northern mixed grass varies spatially and intra-annually, affecting seasonal grazing opportunities for livestock .

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)0.001
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 score0.034
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.225
Teacher spread0.208 · 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

Citations15
Published2015
Admission routes2
Has abstractyes

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