Campus ecology network biodiversity data: York University and The University of Toronto Mississauga
Bibliographic record
Abstract
A university campus provides an opportunity to explore biodiversity, change, and citizen science. Many course offerings such as ecology, experimental design, or environmental science include a laboratory component. York University and The University of Toronto Mississauga collaborated and developed a collaborative platform entitled 'The Campus Ecology Network'. The goal was to connect the data that undergraduate students at each campus collected during hands-on, field exercises. We adopted the same protocols at each campus, and students surveyed each campus in the Autumn, i.e. Fall Term, in 2016. Students used transects to identify sampling locations blocked by major habitat types such as forest, grassland, disturbed sites (i.e. areas with high foot traffic but vegetated), and impermeable sites. Quadrats were then subsequently used to to explore vegetation - 0.5m x 0.5m quadrats to record herbaceous plants and grasses. On these same transects, the total number of vertebrate animals (including humans) were also recorded during the 3-hour sampling instances. Pan traps and sweet nets were used to assess invertebrate diversity. The primary focus was to document structural and species diversity patterns not composition. The data included species richness for key taxa, native versus exotic plants, canopy cover, ground cover, and total number of flowers at that point in time within each quadrat. These data can be used to explore the intermediate disturbance hypothesis, relationships between richness of different taxa, and canopy/ground cover influences on richness. Longitudinal change can also be examined, and the start point of each transect was also georeferenced for mapping or additional research.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".