Influence of organic manures on soil nutrient content, microbial population, yield and quality parameters of pomegranate (Punica granatum L.) cv. Bhagwa
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
During the last era in India, the use of chemical fertilizer has increased tremendously. The excessive use of these chemicals leads to the degradation of soil quality, health, as well as nutritional status. These are also causing a degradation of human health. This experiment was conducted during Mrig Bahar (July to December) during two consecutive years (2019-2020)in a randomized block design with three replications in which six-year-old 42 pomegranate plants were tested with 14 treatments of different organic manures. Findings showed that in the various treatments, the organic combination T13-Jeevamrut 16.08 L plant-1 + Vermicompost 24.79 kg plant-1 had a significant effect on the nutritional status (available nitrogen, available phosphorus, and available potassium) and microbial population (fungi, bacterial, and actinomycetes count). In addition, T13-Jeevamrut 16.08 L plant-1 + Vermicompost 24.79 kg plant-1 found a significant effect on fruit yield characteristics like fruit plant-1 (122.00), fruit yield (17.38 kg plant-1), fruit weight (192.50 g) and fruit quality characteristics such as fruit juice percent (52.92%), and total sugar (11.92%).
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".