Bibliographic record
Abstract
Some urban green spaces (UGSs) are more likely to be invaded (‘invasibility’) than others. This context-dependency is because green space design differs widely in their economic input, management intensity, and plant species diversity. Higher invasibility increases the chance of successful invasions, prompting calls for control efforts in UGS. Yet, it is not clear what factors promote higher invasibility in UGS design. Here, I examine how an iterative process of rototilling, cover crops and sowing of native seed mixes prevents incipient biological invasions within Toronto (Canada). In Chapter 2, I conducted an observational study where I sampled soil seed banks from two restoration stages – newly-established once the restoration process has been completed, and older restored sites (5-8 years). Restored sites had a 9-fold increase in the proportion of native seed mix species relative to newly established sites, suggesting multiple rounds of rototilling may have depleted the invasive seed bank and provide capacity for the native soil seed bank to build-up. In Chapter 3, I designed an experiment to manipulate seed bank density and richness from curated native seed mix species on the germination and biomass of a targeted invader, Cirsium arvense. Higher native seed density and richness negatively impact only invader biomass through different indirect pathways. These findings suggest a dense, multifunctional, and species-rich seed mix can prevent incipient biological invasions at an early plant stage. In Chapter 4, I surveyed mature plant communities in the same plots as I sampled soil seed banks. Using composite metrics that combine community biomass and species richness, invasibility was negatively correlated with denser seed banks, although this is mediated by soil manipulation. In Chapter 5, I explored landscape scenarios on how neighbouring UGSs facilitate the dispersal of Vincetoxicum rossicum seeds as changes in potential functional connectivity. Using habitat suitability and connectivity modelling, most sections of the Meadoway and surrounding landscape matrix represents resistance barriers (e.g., impervious surfaces), corresponding with a small negligible impact from additional UGS development. In sum, I show that invasibility within urban meadows depends on the soil seed bank, planned soil manipulation from rototilling, and competition from native seed mix species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".