Scientific Basis for EcoVeg
Bibliographic record
Abstract
We summarized the scientific basis for EcoVeg, a physiognomic–floristic–ecological classification approach that applies to existing vegetation, both cultural (planted and dominated by human processes) and natural (spontaneously formed and dominated by non-human ecological processes). It provides a framework that can: describe vegetation types at multiple thematic scales, from formations (biomes) to fine-scale associations (biotopes). inventory vegetation and ecosystem patterns within and across landscapes and ecoregions. support status and trends of ecosystems. facilitate interpretation of long-term and short-term vegetation change. track ecosystem responses to invasive species, land use, and climate change. EcoVeg currently guides the U.S. National Vegetation Classification (NVC), Canadian NVC, Bolivian NVC, and the International Vegetation Classification (IVC), including North America, South America, Africa, and all grasslands. Asian elephant passing through a tea plantation (cultural vegetation) in the Valparai plateau in Anamalai Hills of the western Ghats, India, on its way from one natural forest patch to another. Classifying the type of cultural vegetation is important to the overall assessment of elephant habitat, because, although the elephants are able to use the tea plantations as part of a migratory corridor, they are also likely to run into conflict with humans as they pass through (Sukumar and Easa 2006). Photo by Kalyan Varma; used with permission. This photograph illustrates the article “EcoVeg: a new approach to vegetation description and classification,” by Don Faber-Langendoen, Todd Keeler-Wolf, Del Meidinger, Dave Tart, Bruce Hoagland, Carmen Josse, Gonzalo Navarro, Serguei Ponomarenko, Jean-Pierre Saucier, Alan Weakley, and Patrick Comer, tentatively scheduled to appear in Ecological Monographs 84(4), November 2014. http://dx.doi.org/10.1890/0012-9623-90.1.87
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".