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Record W4321842606 · doi:10.32649/ajas.2010.29290

Effect of phosphate fertilizer in the ratio in percentage of Infection on corn Sesamia cretica Led. And relationship to yield production

2010· article· en· W4321842606 on OpenAlexaff
Ali F. Hassin, Naser M. Naser, Hussam F. Najeed Alawadi

Bibliographic record

VenueAnbar Journal of Agricultural Sciences · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSesame and Sesamin Research
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsFertilizerPhosphate fertilizerYield (engineering)AgricultureAnimal scienceAgronomyPhosphateMathematicsToxicologyBiologyEcologyPhysics

Abstract

fetched live from OpenAlex

Experiment was made in AL- Qadisiyah province on maize during the autumn season 2007, to know the impact of four levels of phosphate fertilizer (zero, 160, 200, 240 kg P2O5/ h) and two batches.First add all the amount of phosphate fertilizer at one time when agriculture, and the second was added in two waves, when agriculture first and the second after (45 days) from the first installment. Used in the experiment design sectors indiscriminate full R.C.B.D. Used the seeds of Class of (IPA 3003) parents of hybrid maize. Results showed that the level fertilizer 240 kg P2O5/ ha in reducing the incidence of insect after (8, 10, 12 weeks) and at harvest amounted to (2.34, 4.00, 7.86, 15.26)%, respectively, compared to the treatment of comparison and level fertilizer 160 kg P2O5/ h which gave the highest rate of incidence of this insect, also gave the same level fertilizer increase in the sum total of the plant and the winning 5,648 and 6,141 and 7,288 and 8,056 tons / h respectively in the above levels of fertilizer.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.297
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2010
Admission routes1
Has abstractyes

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