Identifying multidisciplinary research gaps across Arctic terrestrial gradients
Bibliographic record
Abstract
Abstract Global warming is driving environmental change in the Arctic. However, our current understanding of this change varies strongly among different environmental disciplines and is limited by the number and distribution of field sampling locations. Here, we use a quantitative framework based on multivariate statistical modeling to present the current state of sampling across environmental disciplines in the Arctic. We utilize an existing database of georeferenced Arctic field studies to investigate how sampling locations and citations of disciplines are distributed across Arctic topographical, soil and vegetation conditions, and highlight critical regions for potential new research areas in different disciplines. Continuous permafrost landscapes, and the northernmost Arctic bioclimatic zones are studied and cited the least in relation to their extent in many disciplines. We show that the clusters of sampling locations and citations are not uniform across disciplines. Sampling locations in Botany and Biogeochemistry cover environmental gradients the best, and Microbiology, Meteorology, Geosciences And Geographic Information Systems/remote Sensing/Modeling have the worst coverage. We conclude that across all disciplines, more research is needed particularly in the Canadian Arctic Archipelago, northern Greenland, central and eastern Siberia, and in some disciplines, in Canadian mainland, central Alaska, western Siberia and northern Taimyr region. We provide detailed maps of potential new sampling locations for each environmental discipline that consider multiple variables simultaneously. These results will help prioritize future research efforts, thus increasing our knowledge about the Arctic environmental change.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.016 |
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; both teacher heads agree on what is shown here.
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".