The Importance of Landscape Age in Influencing Landscape Health
Bibliographic record
Abstract
ABSTRACT: Ancient landscapes dominate many parts of the world and are common in Australia—do they have a future for continued agricultural production and the supply of ecological goods and services? The hypothesis is that old, weathered landscapes respond differently from young landscapes when subjected to intensive landuse. The major difference in response is that system function regresses or fails in old landscapes. The aging phenomenon is illustrated using data from a podzol chronosequence on coastal sand dunes at Cooloola, Queensland, Australia. The old systems here are shown to regress naturally from forest systems to health systems as access to nutrients decreases. Responses to landuse disturbances in old landscapes other than sand dunes, show analogous regressive trends to the dune landscapes, but the biophysical nature of the responses can vary. How can such trends in landscape health be measured to better link land capability with landuse? The concept of sustainability may not be appropriate for old landscapes and a different framework based on a health paradigm is suggested. Starting from an equilibrium perspective, we move to a conditional stability concept and then to a conditional health paradigm. Health and ill‐health are deemed to be definable within predetermined limits, that is a compliance measure, similar to the diagnostic approach in human medicine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".