Cross-Boundary Sustainability: Assessment across Forest Ownership Categories in the Conterminous USA Using the Montréal Process Criteria and Indicators Framework
Bibliographic record
Abstract
The conservation and sustainable management of forests across ownership groups of the conterminous USA was assessed using the Montréal Process Criteria and Indicators (C&I) framework using national forest inventory and other data. Sixty-one percent of the forest area in the USA is Privately owned (20% Corporate, 39% Family, and 2% Other Private), 37% is Public (28% Federal, 2% Local, and 7% State), and 2% is within Native American Tribal Reservation boundaries. There are many commonalities across ownership categories, but there are also important differences. A 1.1 million ha yr−1 decrease in Family forestland and a 1.0 million ha yr−1 increase in Corporate forestland (C&I 1.1.a) between 2012 and 2019 are among the main trends with implications for sustainability and influence all other aspects considered through the C&I. The majority of annual timber harvests (C&I 2.d) comes from Corporate (46%) and Family (42%) forestlands. Of the most common species, net growth to removal ratios (C&I 2.d) are less than 1.0 for three species on Tribal forestland, two species on Federal forestlands, and two species on Corporate forestlands. Disturbances (C&I 3.a and 3.b) are relatively common (ranging from 4 to 15% of forestland within an ownership category) across ownership categories with the highest proportion of disturbances being caused by diseases and pests on Federal forestland. Differences in the legal and institutional frameworks across ownership categories (Criterion 7) influence how the forest resources can be managed and how policies, programs, and services can be designed and implemented to help maintain and enhance the flow of forest-based goods and services. This analysis helps illustrate that sustainability is complex, C&I are imperfect, and there are additional elements, such as recreational access and ownership/management objectives, that would be helpful for comparing across ownership categories. But the Montréal Process C&I framework helps elucidate the relative threats among ownership categories, in particular the loss of Family forestland to non-forest uses and the increase in disturbances across most ownership categories, and the relative opportunities across ownership categories, including the sustainable supply of timber from Corporate and Family forestlands and the relatively high tree, and presumably broader, biodiversity especially on Local and Other Private forestlands.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".