Arctic vegetation archive and Classification Workshop, Prague, 30–31 March 2017
Bibliographic record
Abstract
The two-day Arctic Vegetation Archive and Classification Workshop, in which twenty-nine individuals (two in absentia) from 9 countries (EU: Czech Republic, Germany, Norway, Slovak Republic, Switzerland, The Netherlands; Russia, USA, Canada) participated, took place at the Czech Academy of Science Building, Prague, Czech Republic, on 30–31 April 2017. An Arctic Vegetation Archive (AVA) is essential for developing an Arctic Vegetation Classification (AVC) and is needed for a variety of international Arctic initiatives that involve Arctic vegetation information. The AVA will gather vegetation and environmental data from approximately 31 000 legacy vegetation plots into a standardized format for vegetation classification and analysis. The primary goal is to develop a strategy for each country to assemble its own archive with common protocols that will later allow the databases to be united into a single AVA using TurboVeg v3 and then use JUICE software to create a Pan Arctic vegetation classification. Several overview and keynote talks set the stage. We reviewed the datasets and plots that are available for each of the floristic provinces in each circumpolar country. Discussions focused on the exchange of data between different database approaches, reflections on the realization of a pan-Arctic vegetation classification, steps still needed to achieve the AVC. At the end of the meeting, the assembled members resolved to accomplish the following within 5 years: – develop a checklist of existing described Arctic vegetation habitat and vegetation types (an Arctic prodromus) according the European Vegetation Classification approach. – develop and use standardized plot-data collection and archiving methods modeled after the European Vegetation Archive and the Alaska Arctic Vegetation Archive. – modify the existing vector-based Circumpolar Arctic Vegetation Map to a raster-based format with 12.5-km resolution, and incorporate modifications based on new knowledge. – work with the Arctic Data Center (ADC) to develop data-sharing methods and rules for Arctic vegetation data. – contribute to training a new generation of young professional Arctic botanists and vegetation scientists through international field courses at the University of the Arctic and the Association of Polar Early Career Scientists (APECS). There was understanding of the necessity to develop a funding strategy to secure funds for completing the AVA and AVC. Finally we resolved to meet again at Arctic Science Summit Week 2019 in Arkhangelsk, Russia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.079 |
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".