Managing for Sustainability: Ecological Footprints, Ecosystem Health and the Forest Capital Index
Bibliographic record
Abstract
We may define ecosystems most simply as the interactions of plants and animals with their abiotic environment.As such, we may identify their ecological character in terms of species dominance (plant or animal), energy flux, nutrient flows and the like.Humans are part of these systems, and we may thus also identify ecosystems in terms of their socio-cultural characteristics.Indeed, increasingly, humans have so modified nature, that the socio-economic (and hence culturally determined) impacts are now the dominant force in ecosystem dynamics (Vitousek et al., 1997).Further, increasingly the landscape has been transformed by wholly human-constructed and maintained ecosystems: e.g.agro-ecosystems, agro-forestry, aquaculture, dams (creating mammoth lakes), diversions for rivers (for irrigation, and/or energy) and so forth.Even without human influence, in the time before Homosapiens and our immediate progenitors, two to three million years ago, ecosystems were anything but static.Over various time scales, from geological to ecological, ecosystems undergo change, owing to geological, ecological and evolutionary forces.Large inland seas, which once covered two-thirds of what is now North America, transformed into fertile plains and grasslands.Continents have formed, and migrated over the Earth's surface -propelled by the geological forces of plate tectonics.In these migrations, tropical ecosystems have become arctic, or sub-arctic.Forests have been gained and lost, lakes appear and disappear, and sometimes connect with and disconnect from the sea -as is the history of the great expanse of waters now known as the Baltic Sea.Today, there is no evidence that these 'larger forces' have been quieted.No doubt they continue to come into play, at gigantic spatial and temporal scales.However, it is evident that at infinitely smaller ecological time scales, 268
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".