Evolution of Lippia multiflora Biomass by the Fertigation Technique on a Ferralsol in the South of Côte d’Ivoire
Bibliographic record
Abstract
In order to domesticate Lippia multiflora for its perpetuation, study was carried out at the National Center of Floristics (CNF) of the University Félix Houphouët Boigny of Côte d’Ivoire. The objective of this work is to estimate the biomass of Lippia multiflora (Verbenaceae), called “savannah tea”, under the effect of urea diluted in water (fertigation) over time on a ferralsol in southern Côte d’Ivoire. The trial was set up in a completely randomized Fisher block design with three replications. Potted Lippia multiflora plants received two doses of urea T1 (0.5 g) and T2 (1 g) previously diluted 1 liter of water and a control treatment T0 without urea addition. The effects of these different doses on the growth parameters of Lippia multiflora were compared with each other using analysis of variance to assess the growth parameters of the plant. The observations were made on the average height of the plants in centimeters (cm), the average diameter of the stem, the average number of leaves and roots. From the results obtained, the contribution of urea influenced the growth of Lippia multiflora because different from those of the T0 control. It appears that the application of urea T1 (0.5 g) best promotes the growth of Lippia multiflora.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".