Workshop on biodiversity and functioning of arctic ecosystems — continuing work on the «Arctic Vegetation Archive» (Arkhangelsk, 21–23 May 2019)
Bibliographic record
Abstract
The three-days Arctic Vegetation Archive and Classification Workshop, in which 32 participants from 9 countries (Canada, Finland, Germany, Italy, Norway, Republic of Slovakia, Russia, Switzerland, USA) participated, took place at the Northern Arctic Federal University, Arkhangelsk, Russia on 21–23 May 2019. The participants reviewed success in archiving data into the AVA and regional Archives, which has been achieved in the last 2 years. International Archive already contains large number of datasets, which allowed to define the ways to use this data for the assessment the dynamic of vegetation due to climate change. Discussion was also focused on the results of regional classification with an attempt to come up with a common approach. During the breakout session, attention was brought to the necessity of international communication: everyone agreed that developing a network will make cooperation easier. At the end of the meeting on 23 May the participants stated long-term goals for the next 4 years: Integrate Russian data entries into AVA by Komarov Botanical Institute and A. N. Severtsov Institute working groups; Develop standardized methods for surveys, archiving and classification; Establish the system of databases management and rules for sharing data; Create a central website containing basic information about national Archives, georeferences and links; Establish funding to complete AVA, AVC and the website. Next meeting will take place at Arctic Science Summit Week in Portugal 2021.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".