Agriculture students’ weed collections: Choices of plants and errors in identification
Bibliographic record
Abstract
Abstract Requiring students to create weed collections is a common technique for teaching weed identification. Data compiled over 18 years from students’ weed collections in a college‐level course included over 350 species of plants. Almost half of the specimens belonged to the Asteraceae or Poaceae. The 30 most frequently collected species accounted for almost two‐thirds of the specimens but Chenopodium album L., the most frequently collected species, accounted for only 4.8% of the total. Overall, 73.1% of specimens were correctly identified to species. Five species (Abutilon theophrasti Medik., Vicia cracca L., Portulaca oleracea L., Plantago major L., and Asclepias syriaca L.) were correctly identified at least 97% of the time. Misidentification was highest with Scorzoneroides autumnalis (L.) Moench [synonym (syn.) Leontodon autumnalis L.], Malva neglecta Wallroth, Erysiumum cheiranthoides L., Echinochloa crus‐galli (L.) Beauv., and Erigeron canadensis L. (syn. Conyza canadensis) and within the genera Sonchus L., Setaria P. Beauv., and Digitaria Haller. Misidentification was the lowest in the Equisetaceae, Apocynaceae, Oxalidaceae, and Plantaginaceae and highest in the Lamiaceae, Poaceae, Brassicaceae, and Asteraceae. Variability in individual species’ morphology may have contributed to misidentification.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".