Climatic Variability in the Kuparuk Region, North-central Alaska: Optimizing Spatial and Temporal Intepolation in a Sparse Observation Network
Bibliographic record
Abstract
Air temperature fields are required as input to spatial models in ecology, geocryology, and biogeochemistry. Air temperature data from a sparse, irregular meteorological network in the Kuparuk region of north-central Alaska were interpolated spatially and temporally to provide a 13-year (1987-1999) series of thawing degree-day fields at 1 km² resolution. Procedures involved standardizing diverse temperature records and applying topographically and climatologically aided interpolation, using station data and digital elevation models, to incorporate the effects of local topography. The accuracy of the interpolation procedures was assessed using cross validation. Considering the number of data points used for interpolation, their distribution, and the size of the area, the combination of climatologically assisted and topographically informed spatial interpolation procedures provides adequate representation of the annual degree-day fields for the Kuparuk region. Spatially integrated mean absolute error does not exceed 3% in any year. To investigate the spatial distribution of interpolation uncertainties, the cross-validation errors obtained at each station for each year were interpolated spatially to a regular 1 × 1 km grid consistent with the degree-day fields.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".