Rescuing Valuable Arctic Vegetation Data for Biodiversity Models, Ecosystem Models and a Panarctic Vegetation Classification
Bibliographic record
Abstract
and biodiversity.Ecosystem models and predictive models make up an important part of CBIO-NET's activities.A wide variety of species distribution modeling tools are already available and can be applied to predict historical, present, and future vegetation and plant distributions.These data can help refine predictions of ecosystem change, such as gas exchange between tundra vegetation and the biosphere.New advances in these methods offer the possibility to incorporate information on biotic interactions (Wisz et al., 2013) and phylogeographic history (Espindola et al., 2012) to fill gaps in information about distributions over space and time.Addressing biodiversity questions in the Arctic is a challenging task, however, because the information on vegetation patterns, which is essential to quantify speciesenvironmental relationships and make ecosystem-level predictions, contains large gaps.The large body of vegetation plot data collected across the Arctic during the past century could provide a key missing link needed to derive predictive models of future distributions under different climatechange scenarios.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".