Data from: Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944
Bibliographic record
Abstract
Paper Abstract: Urban expansion, especially suburbanization, represents a major social, economic and environmental shift that has escalated since the mid-1900s in North America. Suburban development leads to corresponding changes in the treed environment of urban-rural fringes. It is important to understand where, when and why trees change in response to development over many decades, but this is difficult since long-term data are scarce. We used 70+ years (1944–2017) of leaf-off aerial photographs, often representing the only long-term landscape record, to quantify and map spatio-temporal changes in tree density through the entirety of the agricultural-suburban transitional period. Photo-interpretation of individual tree locations, along with recording observable drivers of change, was completed across six different modern landscapes in Mississauga, Ontario, Canada. Results indicate that tree density often recovers, or even increases, post-development. There are differences between landscapes, but most tree density gains are associated with forest expansion and tree planting, while most losses are associated with building and road construction. The influence of these drivers, along with the temporal trajectory of tree density changes, are shaped by a landscape’s socioecological legacy and the length, scope and intensity of development (as decided by decision makers). Processes include initial tree losses followed by recovery from tree planting, and forest succession in abandoned fields after land purchase and nearby development. We assert that the spatio-temporal changes in tree density and related drivers quantified here can be generalized to gain knowledge on how tree density and distribution across agricultural landscapes will change under different development scenarios. Data details: See paper: Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944 - ScienceDirect See code on GitHub: ZZMitch/SuburbanizingTreeDensity_1944to2017: Code from "Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944" (L&UP, 2019) (github.com) - Note: High resolution imagery is not included in this repository. If you use these data, please reference: Bonney, M.T., He, Y., 2019. Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944. Landscape and Urban Planning 192, https://doi.org/10.1016/j.landurbplan.2019.103652.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".