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Record W1964119380 · doi:10.1016/j.scienta.2009.05.024

Effects of repeated applications of municipal solid waste compost and fertilizers to three lowbush blueberry fields

2009· article· en· W1964119380 on OpenAlexafffund
P. R. Warman, J. C. Burnham, Leonard J. Eaton

Bibliographic record

VenueScientia Horticulturae · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsNova Scotia Department of Agriculture
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsCompostLoamFertilizerNutrientRandomized block designAgronomyGreen wasteSoil conditionerMunicipal solid wasteEnvironmental scienceChemistryHorticultureSoil waterWaste managementBiologySoil science

Abstract

fetched live from OpenAlex

Field experiments were initiated in May 1999 and continued through 2002 to investigate the application of municipal solid waste (MSW) compost and fertilizers to lowbush blueberry (Vaccinium angustifolium Ait.) fields. Three sites were selected: Debert, N.S. (Truro sandy loam) and two sites near Musquodoboit, N.S. (both Rawdon gravely loamy sands). Treatments at each site were in a randomized complete block design with six treatments (control [no fertilizer], NK fertilizer, NPK fertilizer, and three rates of MSW compost) blocked four times. Compost treatments provided the equivalent of 100, 200 and 400 kg ha−1 of total N. The experimental objectives were to evaluate soil and plant response to the compost and to determine whether it could be used as an alternative to the traditional chemical fertilizers. Yield, soil fertility, and leaf and fruit nutrients were examined following the four years of treatment applications. The MSW compost had a significant effect on pH and soil extractable levels of P, K and Ca, and influenced N and K levels in leaf samples. Fruit yield and nutrient content, except for Mn, were not affected by the treatments. In general, the compost treatments provided equivalent amounts of plant essential nutrients without negatively influencing trace element absorption.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.255
Teacher spread0.243 · 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 designBench or experimental
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

Citations22
Published2009
Admission routes2
Has abstractyes

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