Bibliographic record
Abstract
The purpose of our study was to understand plant–plant interactions, namely competition among grassland flowering plant species for resources, since we observed different abundance of species at different sites. We also examined how other factors such as temperature influenced grassland communities. The study site was in south side of York University in Toronto, Canada. This site is of importance due to its uses such as electric poles for city power supply, recreation and transportation. However, what was most important about it for us was its biodiversity. Although placed in the city its biodiversity and different vegetation patches of the site allowed us to conduct our data collection at this site. Our group started with a pilot study and the process of data collection followed for the next three consecutive weeks, which started on 2/10/2014. In our study, we examined the relationship between flowering plant species abundance and distance from the shrubs. We randomly chose shrubs and at distances of 1meter, 3meters and 5meters away from the shrubs used transect to take a sample of what different plant species and how many of each existed. To get a thorough idea of what was really happening and understand the effects of all factors combined we also measured and recorded the temperature at each distance and measured the dimensions of each shrub to test if there were any correlations between them. To recognize flowering plant species’ names, the plants book provided in the lab was used as a guide. To examine the effect of distance on the abundance of flowering plants, one-way ANOVA test was performed on the obtained data. Since ANOVA controls Type 1 errors, it was a good choice for our experiment purposes because it could ensure us that any significant result we found was not just down to chance. The means of samples were calculated, and variation between and within groups were calculated; and finally the calculated value was compared to the Fcritical value to test our hypothesis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".