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Record W3035975977 · doi:10.33584/jnzg.2017.79.551

Lime application can help protect pastures against black beetle

2017· article· en· W3035975977 on OpenAlexfundno aff
P.J. Gerard, Deborah J. Wilson

Bibliographic record

VenueJournal of New Zealand Grasslands · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsnot available
FundersDairyNZGraymontAgResearch
KeywordsLimePastureAgricultureAgronomyEnvironmental scienceRange (aeronautics)BiologyEcologyAgroforestryGeography

Abstract

fetched live from OpenAlex

Abstract Black beetle attacks pasture grasses in the northern and coastal North Island and with a warming climate, the beetle has extended its range and damage has become more prevalent. On-farm investigations into prevention of damaging populations suggested that black beetle density was inversely related to soil pH. Two replicated block experiments, the first in 2013-2015 on two farms, and the second on four farms in 2015-2017 investigated the effects of late spring agricultural lime applications at the rate of 5 tonne/ha on summer black beetle populations. The results showed that lime can help suppress black beetle populations. Importantly, the effect of lime persisted into the second year in Trial 2, preventing larval populations reaching damaging levels of over 40/m2. This adds to the already well-known benefits of lime in improving soil health and pasture quality, vigour and persistence. Keywords: agricultural lime, soil pH

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 score0.304
Threshold uncertainty score0.314

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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