Studying Geographical Distribution Map of Weeds of Irrigated Wheat Fields of Ardabil Province
Bibliographic record
Abstract
In order to identify the density and abundance of weeds in irrigated wheat fields of Ardabil Province, 76 samples of irrigated wheat fields based on cultivation area from all counties of Ardabil province for six years (2001-2006) were selected. The genus and species of weeds from each sampling fields and their population indices density, frequency and uniformity of each species were calculated by using Thomas method. Geographic coordinates of field (Latitude, Altitude and Elevation) were the main coverage and were determined by using GPS. These data were used for producing weed maps using GIS in irrigated wheat fields of Ardabil province. Results showed that bedstraw (Galium tricurnatum), Fumitory(Fumaria vaillanti)and wildradish (Raphanus raphanistrum) were dominant broad leaf weed species and wild oats (Avena fatua), rye (Secale cereal) and mouse foxtail(Alopecurus myosuroides) dominant grassy weeds species in irrigated wheat fields of Ardabil province. Bindweed (Convolvulus arvensis), Canada thistle(Cirsium arvense)and Acroptilon repens were the most important disturbing plants prior to harvesting in irrigated wheat fields of Ardabil province.
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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".