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Record W4327807760 · doi:10.15406/hij.2022.06.00235

A preliminary study on emergence and growth of carrot seedlings in response to varying proportions of vermicompost and copper

2022· article· en· W4327807760 on OpenAlexaff
Tongxin Ye, Mercy Ijenyo, Lord Abbey

Bibliographic record

VenueHorticulture International Journal · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsVermicompostDaucus carotaHorticultureSowingCompostShootSeedlingChemistryNutrientBiologyAgronomy

Abstract

fetched live from OpenAlex

Vermicompost is a nutrient-rich amendment commonly used to restore soil health such as restoration of trace element contamination of growing media. A pot study was carried out to determine vermin compost and copper (Cu) interaction on little finger carrot (Daucus carota cv. Nantes) seedlings emergence and plant growth. The treatments were varying rates of Cu (i.e., 0, 100, 200 and 300 mg Cu l-1) and vermicompost (i.e., 0, 25, 50 and 75% w/w).At two weeks after sowing, the emergence rate of carrot seedlings in pots with no vermicompost (i.e., control) were 0.22, 0.25, and 0.32 folds higher than those that received the 25%, 50% and 75% vermicompost, respectively. Vermicompost, Cu and their interaction had significant (p<0.05) effects on leaf chlorophyll and anthocyanin contents. The 50% vermicompost combined with the 100 mg Cu l-1 resulted in greater leaf greenness and anthocyanin content. Plant height and number of leaves were significantly (p<0.05)increased by 0.07 and 0.16 folds following the application of the 25% and 50% vermin compost respectively, compared to the control. The 50% vermicompost had a strongly positive impact on the carrot shoot compared to the root. This preliminary study on little finger carrot cv. Nantes seedlings will require further detailed studies to explain vermicompost mitigation of Cu stress on carrot plants and productivity.

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 score0.660
Threshold uncertainty score0.180

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 head0.021
GPT teacher head0.272
Teacher spread0.251 · 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
Published2022
Admission routes1
Has abstractyes

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