Ownership Changes and Harvesting Patterns Associated with the Forest Products Industry in West-Central Alabama from 1984 to 2014
Bibliographic record
Abstract
Since the beginning of the 1980s, vertically integrated forest products companies have divested their forestland with much of the new ownership being real estate investment trusts (REITs) and timberland investment management organizations (TIMOs). These new landowners and their associated behavior of intensive timber management and higher and better use conversion has given rise to issues such as land-use change, fragmentation, and conservation. To better gauge harvesting patterns and ownership changes associated with the divestment of forestland by forest industry and the arrival of TIMOs and REITs on the forested landscape, eleven Landsat scenes were used to detect harvest activity within the Alabama counties of Bibb, Hale, Pickens, and Tuscaloosa from 1984 to 2014. Detected harvesting activity was paired with county parcel data and then classified based on landowner type: REITs, TIMOs, forest product industry, government, and non-industrial private forest (NIPF) landowners. Overall harvest trends showed a decrease in harvest rates from 1984 to 2005 with a slight increase in harvest rates after 2005. Per scene interval, acres harvested were highly variable for NIPF and relatively stable for forest industry during this time. Government ownership maintained relatively low and stable harvesting behavior throughout the study period. Acres harvested by REITs was relatively low. TIMOs showed an ever increasing rate of harvest within the study area until the last scene interval (2011-2014).
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".