Potential contributions of statistics and modelling to sustainable forest management: review and synthesis.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter provides a review of the statistical and modelling disciplines, their techniques and potential contribution to sustainable forest management (SFM). The main topics covered are: Mensuration and models for sustainable forest management (SFM) Inventory and monitoring for forest sustainability: criteria and indicators Models of tropical forests for the conservation of biodiversity Integrating information and models across spatial and temporal scales for SFM Climate and carbon models in relation to sustainability New techniques for the statistical analysis of sustainability data Uncertainly analysis in modeling and monitoring for SFM Forest data, information and model archives There are major contributions to be made, in particular in the areas of information and model integration where a synthesis of information and models across both spatial and temporal scales is required. There is a great need for international collaboration on the development of open and shared forest data and model repositories/archives, as well as continued development of forest information systems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it