Bibliographic record
Abstract
For decades, forests and the forestry industry have been at the epicenter of global environmental politics. During the 1990s the world’s forests disappeared at a rate of around 13 million hectares per year, with tropical forests in South America, Africa, and Southeast Asia especially hard hit. 1 This pattern continues in the new millennium: the countries with the largest net forest loss per year in 2000–2005 are concentrated in tropical Africa, South America, and Asia. 2 Today, less than four billion hectares of forest remain on the planet, much of it concentrated in northern temperate areas in Russia, Canada, and the United States. Since 2000 the rate of forest loss worldwide has declined, largely due to the expansion of forest farms in Europe and Asia. In recent years total forest area has grown in Europe, East Asia, and North America, but this growth is offset globally by continued rapid deforestation in South America and Africa. 3 As the availability of naturally grown tropical hardwoods declines, the value of this wood increases, driving the axes and bulldozers deeper into these forests. The great majority of tropical rainforest that remains on the planet lies within the borders of nations with poor records of environmental protection. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.046 | 0.014 |
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".