Respon Pemberian Pupuk Cair Fitofit Terhadap Pertumbuhan dan Produksi Beberapa Varietas Jagung Hibrida (Zea mays)
Bibliographic record
Abstract
BackgroundSweet corn (Zea mays saccharata Sturt) or better known as sweet corn is one of the most famous horticultural commodities in the United States and Canada.Sweet corn has been known in Indonesia since the 1970s (Syukur, 2013).The public increasingly favors sweet corn because it has a sweeter taste, more fragrant aroma and higher nutritional content.Sweet corn is usually served in the form of corn on the cob, roasted corn, corn sugar, corn milk, cakes and corn chips.Sweet corn is also very good for diabetics because it contains low levels of sugar and fat.Sweet corn seeds are rich in sugar content and calories compared to other vegetables.In 100 grams of fresh sweet corn seeds contain 86 grams of calories, 2 grams of fiber or about 5% of the daily dietary fiber requirement and about 6% of the daily vitamin requirement.Sweet corn contains a lot of free sugar and starch.The sugar content in sweet corn is not glucose or sucrose, but in the form of fructose, a type of sugar polymer known as fruit sugar (Dongoran, 2009).Sweet corn production in Indonesia in 2013 was 18,506,287 tons, decreased by around 670,743 tons compared to sweet corn production in 2012 which was 19,377,030 tons, in 2014 it was 19,033.000 tons and in 2015 it was 19,610,000 tons (BPS, 2016).The more comprehensive the community's knowledge of the sweet corn plant, the more people's demand for this sweet corn plant will increase.The production of sweet corn does not match the increasing demand for sweet corn.Sweet corn productivity in Indonesia averages 8.31 tons per ha.Meanwhile, the potential yield of
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.017 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".