Addendum to “Clearing, grazing and reservation: assessing regional impacts of vegetation management on the fauna of south western New South Wales”: the species assemblages
Bibliographic record
Abstract
Ellis et al. (2007) classified the fauna of south western New South Wales into a management related structure of specialised and generalist species assemblages. The broad description of this system was presented in the paper but the detailed assignment of species to the various groups and the relationships between the groups was not published when supplementary material was not produced with the book. To prevent the loss of this information, which is important for applying the results of this study to other situations, the assignment of species to broad management groups and assemblages is now published as Table 1 (below). The relationship between specialist assemblages, which would be actively conserved through appropriate vegetation and/or water management, and their relation to vegetation types was presented in the original publication. The relationship between vegetation types and generalist assemblages, which would accrue benefits from actions for specialist assemblages in the same vegetation type, is given in Table 2 (below). Habitat mapping through broad vegetation types was thought not to adequately reflect the requirements of shorebirds and no analysis of their distribution was attempted.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.165 | 0.061 |
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".