Homebuilder Practices and Perceptions of Construction on the Wooded Lot: A Quarter Century Later Follow-Up Assessment
Bibliographic record
Abstract
Building new homes on wooded lots is common in the upper Midwest, United States. Existing trees are often left behind during construction to become part of the future landscape. A study conducted in 1980 found that homebuilders in Portage County, Wisconsin, U.S. generally had a poor understanding of how construction activities could impact the health of trees intended to be preserved. Researchers replicated that study 27 years later by surveying homebuilders in the same region to see how their tree preservation knowledge and use of construction activities have changed during that time. The results indicate few construction activities changed significantly, showing that little has changed overall to improve tree preservation. Even though builders significantly improved their knowledge of the negative effects that storage of fill soil on roots poses to tree preservation, they also significantly increased usage of that very same activity. Builders almost never consulted a tree preservation expert and thought doing so was the least important activity when making tree preservation decisions. Interest in a tree preservation training workshop was limited. Unless pressured by consumer demand or regulation, builders will probably not improve their tree preservation knowledge, change their construction activities, or include tree experts anywhere in the process.
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) | 0.001 | 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 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".