Telling the Story of Sustainable Forests: Is It More than Publishing Another C&I Report?
Bibliographic record
Abstract
The initial set of national reports on sustainable forests was quite popular and useful in describing the current state of forests from ecological, economic, and social perspectives. In the country reports by Montreal Process countries, information was readily available for some indicators not only for describing the current condition, but also for reporting on recent trends. On other indicators, information had to be collected for the first time to describe the current condition. As countries look forward to releasing the next round of national reports in 2009 or 2010, the fresh data presented will lead to new questions, chief among them, “Are our country’s forests more sustainable today than they were when the last report was published?” In a previous paper (Guldin and Heintz 2006), Ted Heintz and I discussed a set of questions that emerged following the release of the National Report on Sustainable Forests—2003 (USDA 2004). We received a number of comments from scientists regarding the apparent lack of a well-defined model or framework to help readers understand the linkages among indicators. Some of those comments lamented not having a “systems ” model. Ecologists, economists and social scientists each wanted a “systems ” model drawn from their own discipline, thinking that was the most appropriate perspective for evaluating sustainability. In addition to these comments, there
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 | 0.017 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".