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
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".