Science in Forestry: Why does it sometimes disappoint or even fail us?
Bibliographic record
Abstract
Society invests in science to advance human interests and limit human impacts on the environment. However, despite great progress in forest science, governments and forest companies remain reluctant to invest adequately in research. We believe this reflects the perception that forest science serves itself more than forestry, and that one determinant of this perception is a misunderstanding of science. Science involves knowing, understanding and predicting. Many feel that only the reductionist, disciplinary, hypothetico-deductively-derived understanding component is hard science. Inductively derived knowledge and experience are often regarded as soft science. Predicting future states of forests involves complex hypotheses that are not amenable to traditional hypothesis testing and, according to some, this renders prediction of complex systems soft science. Science-based forest policy frequently employs hard science: the understanding component of science based on reductionist, jigsaw puzzle research. Necessary for the development of SFM, this is not sufficient for reliable prediction of possible forest ecosystem futures, for which knowledge and understanding must be synthesized into decision support systems at appropriate temporal, spatial and complexity scales. These should be linked to visualization software to create a common language by which to communicate to a diversity of audiences the available choices and their possible consequences. Key words: science in forestry, forest policy, ecosystems, prediction, decision support systems, visualization, “jigsaw puzzle” science, hard science, soft science
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".