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Record W11610726 · doi:10.1177/036319908000500103

Comparative nitrogen partitioning and water use by native and introduced grass communities in southern Alberta, Canada

2005· dissertation· en· W11610726 on OpenAlexaboutno aff
Shane Warren. Porter

Bibliographic record

VenueJournal of Family History · 2005
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMonoculturePerennial plantAgronomyEnvironmental scienceGrasslandGrowing seasonBiology

Abstract

fetched live from OpenAlex

The objectives of this research were to evaluate 1) short-term changes in soil and plant N partitioning created by cultivating and re-seeding native grasslands with two cropping systems of wheat and perennial (crested wheatgrass and Russian wildrye) monocultures; 2) differences in the rate of soil water uptake between Mixed Prairie grasslands, crested wheatgrass and Russian wildrye after a dry-down period; and 3) differences in above ground water use efficiencies, root and crown masses between Mixed Prairie grasslands, crested wheatgrass and Russian wildrye under two different soil water contents. The perennial agronomic species were recommended by Agriculture and Agrifood Canada for seeding in Mixed Prairie and Fescue grassland in southern Alberta, Canada. In the first four years after plow-down, soil nitrate (NO3 -) concentration was higher and light fraction N (LFN) was lower in the soil under wheat than native grasslands. Although LFN was lower in perennial monocultures than native grasslands, there was little difference in soil nitrate. More N was partitioned into shoot biomass of wheat, crested wheatgrass and bromegrass that native grasslands and levels increased as annual and long-term growing season precipitation increased. There were no differences in the rate of soil water uptake after dry-down periods between native Mixed Prairie, crested wheatgrass or Russian wildrye, but both perennial monocultures had higher above ground water use efficiencies than native Mixed Prairie.

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 score0.327
Threshold uncertainty score0.342

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 head0.025
GPT teacher head0.216
Teacher spread0.191 · 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
Published2005
Admission routes1
Has abstractyes

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