Arctic Museum collections: Documenting and understanding changes in biological and cultural diversity through time and space
Bibliographic record
Abstract
Arctic Museum collections: Documenting and understanding changes in biological and cultural diversity through time and spaceThe role of museums and herbaria in biodiversity and cultural synthesis efforts in the Arctic is to preserve the physical and cultural specimens and to advance scientific knowledge by improving access to digitized information.The information associated with these collections, such as geographic, cultural, environmental, phenology, and other information about associated organisms, collector field notes, and tissues and molecular data extracted from the specimens is an invaluable resource providing the baseline from which to further biodiversity research and provide critical information about existing gaps in our knowledge of life in the Arctic.Effective management and conservation of high-latitude biological and cultural diversity in the face of unprecedented rates of climate change requires information on past and current patterns of diversity and their evolution.This special issue of Arctic Science presents results from a workshop entitled "Arctic Museum Collections: Documenting and understanding changes in biological and cultural diversity through time and space" held during Arctic Science Summit Week in March 2016 at the University of Alaska Fairbanks.The workshop brought together scientists from many Arctic nations and focused on current informatics infrastructure, developing large networks of Arctic collections collaborators, and engaging new members and their students in many issues relevant to the Arctic and Subarctic.Lewis et al. review the status of knowledge of genetic diversity and evolutionary history for bryophyte taxa as it relates to shifting species ranges and colonization of new habitats.The University of Alaska Museum is a leader in Arctic and Subarctic collections and their digitization (with our multicollection database Arctos, arctosdb.org).Sikes et al. review rapid digitization efforts in entomological collections in the Arctic and highlight the open-access, online presence of the University of Alaska Museum's rapidly growing entomological collection.To answer relevant questions on the impact of change on high-latitude biological and cultural diversity, we need to synthesize and continue to generate baseline information on Arctic and Subarctic systems.Winker and Withrow show the inadequacy of most specimen collections to the task at hand of facilitating biodiversity assessment and conservation using the bird collections at the University of Alaska Museum as an example.They argue that we need to work collectively to fill temporal, spatial, and taxonomic gaps so we can develop and wield the science that will make us better stewards of Arctic environments.Several contributions in this special volume underscore the international, collaborative scope of Arctic research, including a detailed account of the historical exploration and specimen contributions from Finnish botanists and mycologists by Väre, or the description of baseline data acquisition on plant communities on Greenland accomplished through the Greenland Botanical Survey and subsequent field campaigns, the specimens from which are
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".