Agriculture students’ weed collections: Choices of plants and errors in identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it