Bibliographic record
Abstract
Most of this book has addressed active management — taking actions to achieve habitat goals for a species, a community or contributing to biodiversity conservation, while also considering the potential for providing wood and nontimber products for people. But many people do not feel compelled to manage their forests. The millions of small private landowners in the United States and Canada may own their forests for reasons other than timber, woodcock, or deer. They just like to have a forest. To walk through it, see it, sit in it, and listen to the birds in it (regardless of species). Except when there is a disturbance, forests change slowly. They provide a place that evokes stability, security, and spirituality. For people who view forests in this way, management is not only unnecessary, it is disruptive, and evokes instability, insecurity, and flies in the face of personal spirituality. And they may extend those feelings to all forests regardless of who owns them, because, after all, we are merely temporary tenants on earth, regardless of what we pay for the pieces we use. So, for many people, doing nothing is a perfectly acceptable management decision (Kittredge and Kittredge 1998). And doing nothing is indeed a management decision.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.020 |
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".