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Record W2519907133 · doi:10.3968/8486

Assessement of the Relationship Between Increase in Heigth of Cassava Growth Rate and Agro-Climatic Parameters in Ilorin Area of Kwara State, Nigeria

2016· article· en· W2519907133 on OpenAlexvenueno aff
T.I. Yahaya, S Ojoye, Tsado E.K

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsRelative humidityCropAgricultureEnvironmental scienceYield (engineering)AgronomyWind speedGeographyMathematicsBiologyEcologyMeteorology

Abstract

fetched live from OpenAlex

Cassava is primarily produced for food in its various forms and Nigeria has been recognized as the largest producer of the crop in the world. Despite the impacts of various weather parameters on crop production, Cassava can still withstand harsh conditions making it a key crop for protecting small holder farming against climate change. This paper therefore examined the relationship between increase in height of Cassava growth rate and agro climatic parameters. The agro climatic indices appraised were Rainfall, Relative humidity, Temperature and wind speed. Interrelationship between these agro climatic variables and increase in the height of growth rate of Cassava was computed using regression analysis. It was discovered that the four agro climatic variables had relationship with one another at either 95% significant level or 99% level. It was also revealed that there is 75% at 95% significant level in the rate of increase in height and yield of Cassava which was accounted for by relative humidity. It was therefore concluded that increase in the height rate and yield of Cassava due to relative humidity was as a result of combined effects of the three other climatic parameters.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.005
Science and technology studies0.0000.003
Scholarly communication0.0000.003
Open science0.0010.000
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 head0.026
GPT teacher head0.290
Teacher spread0.264 · 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.

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
Published2016
Admission routes1
Has abstractyes

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