Diversity, Species, and Plant Growth Regulator and Nutrient Content of Local Weeds in Tawaeli Sub-District, Palu City, Indonesia
Bibliographic record
Abstract
Each area has a variety and different types of weeds, the differences in each weed are caused by natural factors and human actions, including excessive pesticide spraying. This action can cause the chemical content, namely plant growth regulators and the nutrients in the weed plant to change and cause the quality of a soil to decrease. The research objective was to determine the level of diversity, species, and content of plant growth regulators and nutrient element measurements for local weeds. The research design was an exploration method in community agricultural land areas by recording the number of weeds found in agricultural land areas, trails, and irrigation edges in Tawaeli District, Palu City. Diversity analysis was using the exploration method, and the diversity index of medicinal plants was calculated using Shannon Wiener. The identification of weeds was carried out by UPT Herbarium (Tadulako University), plant growth regulators and nutrient content analysis were carried out using existing methods in the laboratory using local weed samples taken in the field. The results showed that there were 27 species of weeds grew on agricultural land in Tawaeli Sub-District, Palu City. These weed species belonging to 17 different plant families. Based on the category of diversity index, it was known that the biodiversity of weeds at the observation sites belongs to the low class (H' <1.00) and the medium class (H' >3.00). The conclusion was that in general, the high number of species and weed diversity were in the medium class H' category. Good nutrient content in weeds shows the potential of weeds as a source of important nutritional elements may benefit the growth and development of cultivated plants.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".