Marcaccio and Chow-Fraser 2018 Final Report to MTO on treatment efficacy
Bibliographic record
Abstract
Roadside easements are a unique linear habitat that can be easily invaded by invasive Phragmites australis. While many North American jurisdictions have initiated control programs, few have established associated effectiveness monitoring programs. Here, we propose and apply three methods to determine effectiveness of a regional treatment program undertaken by the Ministry of Transportation of Ontario (MTO) in southwestern Ontario. We utilized 1) high-resolution spring orthophotography, 2) medium resolution multi-seasonal satellite image data and 3) high-resolution multispectral satellite image data to assess the effectiveness of MTO’s treatment program. Using digitization and image classification, we deduced effectiveness of treatment programs in over 3,900 km of roadside habitat between 2010-2015 (orthophotography) and 2016-2018 (satellite data). Net decreases in areal cover of Phragmites were over 95% for all road types other than for major expressways, which saw decreases between 80-95% between 2010 and 2015 but only 20-55% between 2016 and 2018. The areal cover of Phragmites also increased more rapidly within untreated expressway habitat compared with other road types over the same time period. Although orthophotography (20-cm resolution) acquired in spring yielded good results for identification of invasive Phragmites, it is only available once every five years on a provincial scale. By comparison, medium resolution satellite data (Sentinel-2) provided good results within large expressways (with larger and wider easements/habitat area) but was poor for all other road types (<2 lanes). These data miss small patches which are confirmed through high-resolution satellite data (Worldview 3; <1.5m). We advocate for use of medium-resolution satellite data for annual baseline information on expressways, and high-resolution satellite data before and after treatment programs to directly assess effectiveness at smaller spatial scales.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".