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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.007 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".