7. Authors 8. Performing Organization Report No.
Bibliographic record
Abstract
By law, Departments of Transportation are required to control noxious weeds along highway rights-of-way (ROWs). Since 2000, District 4 (D4) of Minnesota Department of Transportation (Mn/DOT) adopted a survey design consisting of n = 7, 3-mi segments to quantify infestations of Canada thistle (Cirsium arvense (L.)(Scop.), leafy spurge (Euphorbia esula L.), and poison ivy (Toxicodendron radicans) in chosen regions of the district. In 2004 and 2005, a second survey design was added to see if stratification by ecozone in D4, and greater numbers of 1/4-mi segments could improve precision. Comparison of matching sample statistics from the 3-mi and 1/4-mi plans in each year indicated the two plans yielded equivalent estimates of mean acres per roadway mile of each weed (α = 0.05). However, precision at the district level was much greater in all cases with the 1/4-mi plan. In addition, weed abundances varied substantially among ecozones (α < 0.05); this knowledge will allow managers to direct control efforts to problem areas. Finally, a combination of computer based mapping and resampling of the 1/4-mi segments observed in the two years suggests that additional improvements in precision and efficiency are likely to occur if segment lengths are shortened to 125 ’ or less. Shorter segments would reduce inspection costs, increase sample sizes, improve precision, and possibly allow conversion from an area-measurement approach to
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.777 | 0.841 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".