Bibliographic record
Abstract
In 1787, the U.S. Congress authorized the sale of the "Ohio Company Purchase", ca. 5000 km2 in Appalachian Ohio. The land was surveyed using a township and range system shortly thereafter. Data on >5600 witness trees were transcribed from the survey records, and witness tree locations were plotted on a digital map. This information was used to evaluate presettlement forest composition and structure and to investigate vegetation-site relationships before widespread alteration of the forests had taken place. Presettlement conditions were compared with present conditions using forest inventory and analysis (FIA) data. Two hundred years ago, the forests of southeastern Ohio were dominated by large individuals of Quercus alba L., Carya Nutt. spp., Quercus velutina Lam., and Fagus grandifolia Ehrh. These four taxa accounted for 74% of all witness trees. Although almost 70% of the region is forested today, the second-growth forest has witnessed a decrease in Quercus and Carya and an increase in Acer saccharum Marsh., Acer rubrum L., and many early successional species in smaller size classes. Despite the significant shift in forest composition and structure, species in general seem to be occupying similar positions in the present-day landscape compared with the presettlement forest; topographic variables most strongly control species occurrence in this landscape.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".