Applications of local floras for floristic subdivision and monitoring vascular plant diversity in the Russian Arctic
Bibliographic record
Abstract
The local flora method has been used by Russian botanists for studying vast wilderness areas. The method strives to determine the total flora within a certain limited area and provides comparable data for spatial comparisons between different locations and temporal comparisons at the same location. Complete vascular plant diversity was sampled in 240 localities with an area between 100 and 300 km2 each throughout the Russian Arctic. These data were incorporated in a specially developed Integrated Botanical Information System (IBIS). This database provides a unique opportunity to study spatial gradients of different floristic variables. Pairwise similarity of species composition and proportions of various phytogeographical groups in local floras were used in a floristic subdivision of the Russian Arctic. The floristic units derived by this method often resembled subprovinces of B.A. Yurtsev (1994. J. Veg. Sci. 5(6): 765–776), but there were also several areas of nonalignment. Application of local floras for monitoring of temporal changes has several constraints. However, nine local floras were revisited 20–70 years after the initial survey. Increases in the number of Boreal and Hypoarctic species were recorded in the southern local floras. Standardized methods and the use of modern technical tools for accurate documentation could enable use of this approach at observatories across the Arctic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".