Bibliographic record
Abstract
Forest structure refers to the physical arrangement of all the components of a forest (USDA, 2025). The structure of a forest can be impacted by natural and anthropogenic processes, and thus studying the forest structure of an area can help us better understand the impacts of these processes (USDA, 2025). Belle Park, and it’s adjacent Belle Island, are situated in Kingston, Ontario. Belle Park was built on a wetland, and in 1952 the wetland was converted to a municipal landfill that was in use until 1974. By 1978, the area was converted to a golf course and the land was filled in, and finally in 2017 was turned into the park (BPP, 2024). This study aims to better understand the impacts of these land uses by looking at the forest structure and species in various areas of the park and island that were impacted by different land uses. A point-centered quarter method (PCQM) was used to estimate the number of individuals present in each chosen area (ClintonCC, 2025). Two areas were chosen in the park, and one on the island, and data was collected at 10 equally spaced points along a transect. Based on species, diameter at breast height, and distance from center point, stem density and basal area were calculated using appropriate equations. It is expected to find that there will be differences in the species and the forest structure of each chosen area on Belle Park/Island due to the differences in land uses and human interventions. By understanding the differences in species and forest structure of these different areas, we can aim to better understand the “natural” state of this land in comparison to the disturbed areas, and overall gain a better understand of how this land was impacted by its land uses.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".