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Record W2000263692 · doi:10.1080/00103620902960682

Effect of High‐Molybdenum Compost on Soils and the Growth of Lettuce and Barley

2009· article· en· W2000263692 on OpenAlexafffundabout
Md. Abul Kashem, P. R. Warman

Bibliographic record

VenueCommunications in Soil Science and Plant Analysis · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsNova Scotia Department of Agriculture
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCompostLactucaLoamPhytotoxicityChemistryAgronomyHordeum vulgareSoil waterAnimal scienceHorticulturePoaceaeBiology

Abstract

fetched live from OpenAlex

A pot experiment evaluated the growth of lettuce (Lactuca sativa L.) and barley (Hordeum vulgar) and accumulation of molybdenum (Mo) in plants and soils following amendments of Mo compost (1.0 g kg−1) to a Truro sandy loam. The treatments consisted of 0 (control), 12.5, 25, and 50% Mo compost by volume. The Mo compost did not affect dry‐matter yield (DMY) up to 25% compost, but DMY decreased at the 50% compost treatment. The 50% compost treatments increased the soil pH an average of 0.5 units and increased the nitric acid (HNO3)–extractable Mo to 150 mg kg−1 and diethylenetriaminepentaacetic acid (DTPA)–extractable Mo to 100 mg kg−1 in the growth medium; the same treatment increased tissue Mo concentration to 569 and 478 mg kg−1 in the lettuce and barley, respectively. Plants grown in the 25% compost produced about 55 mg kg−1 of total Mo in the growth medium; this resulted in tissue Mo concentration of 348 mg kg−1 in lettuce and 274 mg kg−1 in barley without any phytotoxicity. Our results suggested that 55 mg Mo kg−1 soil would be an appropriate limit for Mo loading of soil developed from compost additions, a value which is presently greater than the Canadian Council for Ministers of the Environment (CCME) Guidelines for the use of type B compost in Canada.

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)0.002
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 score0.052
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.231
Teacher spread0.223 · 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

Citations5
Published2009
Admission routes3
Has abstractyes

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