Effects of Liquid Nano-Carbon Bio-Fertilizer on the Growth of Spinach
Bibliographic record
Abstract
In order to compare the effects of liquid biological nano-carbon fertilizer, humic acid and urea on the growth of Spinach, a field plot experiment was conducted to study the effects of five different treatments on root length, yield and quality of annual Spinach. Fertilizer had a significant effect on root length of annual Spinach. The order of root length was T3 > T5 > T4 > T2 > T1. The growth rates of roots were 21.69%, 18.94%, 14.26%, 3.76% and 0.0%, respectively. Fertilizer significantly increased the yield of annual Spinach. The order of yield increase was T5 > T3 > T4 > T2 > T1. The yields were increased by 16.66%, 14.71%, 10.55%, 6.44% and 0.0%, respectively. Potato liquid nano-carbon bio-fertilizer has a significant effect on maintaining the growth and increasing yield of Spinach.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".