Which species should be monitored to indicate ecological sustainability in Australian forest management?
Bibliographic record
Abstract
This paper summarises the key findings of a study that investigated the feasibility of developing a practical, sensitive and cost-effective approach to the implementation of Montreal Process Indicator 1.2c for monitoring populations of representative species for forest management. Representative species include those for which a significant change in population levels have a high likelihood of indicating a significant change in populations of other species. This focus on populations of individual species, in addition to habitat surrogates, is needed because managers require confirmation that their actions are having the desired effect and because factors other than habitat availability may interact to account for the size of populations. The study produced 13 collaborative reports and research papers. These included literature reviews identifying species (vertebrates, invertebrates and vascular plants) known to be, or potentially, sensitive to logging in south-eastern Australia, and reviews of the potential for species and functional groups to serve as bio-indicators in monitoring programmes. The study also categorised plant and animal species in terms of their known or suspected sensitivity to logging. Using large retrospective (space-for-time) datasets from Queensland, New South Wales, Victoria and Tasmania, the study analysed correlations between species across a wide range of taxa, and the frequencies of occurrence or abundance of species in relation to logging intensity or time since logging. Principles for consideration in the design of monitoring programmes were proposed and discussed, and a new method (videography) for remotely-sensing habitat (forest structure) attributes important for ground-dwelling mammals (and potentially other fauna) was demonstrated.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".