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Record W2949025093 · doi:10.3390/agronomy9060282

Gypsum and Coal-bed Methane Water Modify Growth Media Properties, Nutrient Uptake, and Essential Oil Profile of Lemongrass and Palmarosa

2019· article· en· W2949025093 on OpenAlexaff
Valtcho D. Zheljazkov, Tess Astatkie, Urszula Norton, Ekaterina A. Jeliazkova

Bibliographic record

VenueAgronomy · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGeraniolTap waterGypsumChemistryNutrientHorticultureEssential oilAgronomyEnvironmental engineeringEnvironmental scienceFood scienceMaterials scienceBiologyMetallurgy

Abstract

fetched live from OpenAlex

Coal-bed methane (CBM), an important energy source, coproduces a vast amount of saline-sodic wastewater, CBM water (CBMW), with environmental and economic disposal issues. This research evaluated under a greenhouse production system the influence of gypsum (CaSO4·2H2O) and CBMW on yields, essential oil (EO) content and composition in lemongrass (Cymbopogon flexuosus) and palmarosa (C. martinii), and on growth medium pH and available nutrients. CBMW treatments had higher pH than tap water treatment. Gypsum reduced pH in all CBMW treatments but did not affect pH in tap water treatment. While CBMW may increase the available Cu and Fe in growth medium, the application of gypsum may negate this effect. CBMW significantly increased growth medium Na. Gypsum increased growth medium S, and CBMW increased S in the high gypsum treatments. Palmarosa height, fresh weight, geranyl acetate, and isoneral in lemongrass EO were reduced, while geraniol in palmarosa EO increased with CBMW relative to tap water. In distillation waste plant tissue, CBMW increased Na in lemongrass and palmarosa by almost eight times; increased total P, S, and Mn in palmarosa; and reduced total N, S, Ca, and Mg in lemongrass and Ca in palmarosa, relative to tap water. This study demonstrated that CBMW may be used for greenhouse production of high-value crops, but it may affect the yields and oil content of some crops and growth medium characteristics.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.219

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 head0.012
GPT teacher head0.178
Teacher spread0.165 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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