On open access, data mining and plant conservation in the Circumpolar North with an online data example of the Herbarium, University of Alaska Museum of the North
Bibliographic record
Abstract
With the advent of global online data sharing initiatives, few limits remain to using the treasure troves of museum data for biodiversity and conservation. The University of Alaska Museum Herbarium is fully online with metadata. Over 260 000 specimens representing the largest collection of Alaska plants anywhere can be data mined. We found that most specimens were collected through the National Park Service’s Inventory and Monitoring program at Denali National Park and Preserve. The majority of specimens were collected along roads, trails, coastline, or waterways, while high-altitude, remote, and pristine sampling locations are underrepresented still. Actual field efforts varied over the years, peaking in the late 1980s. From 1 to 400 specimens were collected per sampling location, and on average 40 species were obtained per collection event at a unique location. Our analysis presents a first data mining inventory of such open access data allowing for a rapid assessment, quality control, and predictive modeling involving automated high-performing machine learning algorithms and mapping analysis using open geographic information systems concepts. Our research sets a first template for more investigations in the Arctic and we briefly compare with selected specimen details from adjacent landscapes such as the Russian Far East, Canada, and the Circumpolar North.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".