Velvetleaf (Abutilon theophrasti Medik.) productivity in competitive conditions
Bibliographic record
Abstract
Velvetleaf (Abutilon theophrasti Medik.) is an invasive alien species in many countries and one of the major weeds in summer row crops worldwide. Weed-management techniques that reduce weed production need to be investigated to provide new approaches. The first step in this process is the determination of weed productivity in different competitive conditions. Field experiments were conducted in 2006 and 2008 in an experimental field in Padinska Skela to quantify growth and seed production of velvetleaf in maize, as well as in a velvetleaf monoculture. A density of velvetleaf ranging from 1 to 8 plants m-1 was artificially created. In a mixture with maize, velvetleaf was sown in crop rows. The growth of velvetleaf was estimated based on plant height, fresh aboveground biomass and leaf area index (LAI). Velvetleaf fecundity was determined as seed mass plant-1 and seed mass m-2. Differences between years in plant production were very prominent. In general, velvetleaf productivity in maize depended on its density. Intraspecific competition had a major influence on growth and seed production when velvetleaf density was from 4 to 8 plants m-1 in maize rows. This information indicates that environmental conditions and weed density can promote/reduce inter- and intraspecific competition and help in the construction of population dynamics models to predict population density, seed bank and competitiveness of weeds and reduce inputs for weed management.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".