Effectiveness and cost of a rapid response campaign against Japanese knotweed (<i>Reynoutria japonica</i>) along a Canadian river
Bibliographic record
Abstract
Abstract Japanese knotweed (Reynoutria japonicaHoutt.) is an invasive Asian plant abundant along rivers in its introduced range. In riparian areas, floods and ice flows uproot the rhizomes, facilitating their dissemination downstream. Control of large, well-establishedR. japonicaclones in riparian areas is difficult if the use of herbicides is prohibited. An alternative to controlling entrenched clones is the rapid detection and manual unearthing of rhizome fragments that have recently rooted after being deposited by floodwaters. We applied this strategy along a Canadian river where spring floods with abundant ice are recurrent. Two river stretches, with approximately 10 km of shoreline each, were selected for the fragment removal campaign. One of the stretches was heavily invaded byR. japonica, while the other was only sparsely invaded. In the heavily invaded stretch, 1,550 and 737R. japonicarhizome fragments were unearthed in 2019 and 2020, respectively. Unearthed fragments had an average length of 27 to 32 cm. Only 21 fragments were found in the sparsely invaded stretch in 2020. Despite similar distances being surveyed, the detection and unearthing took 62% less time (overall) in the sparsely invaded than in the heavily invaded stretch. Along sparsely invaded riverbanks, a rapid response removal campaign forR. japonicacost, including transportation and labor, an estimated Can$142 (US$105) per aborted clone (i.e., fragment removed). A rapid response removal campaign is economically advantageous compared with the hypothetical eradication of large, well-established clones, but for it to be cost-effective, the time spent locating rhizome fragments must exceed the time spent unearthing them. The question is not whether rapid response unearthing is economically feasible—it is—but rather what invasion level renders the intervention practicable. In highly invaded river stretches generating thousands of fragments annually, finding and removing these fragments year after year would require a massive, unsustainable effort.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".