GIS-based model of stable hydrogen isotope ratios in North American growing-season precipitation for use in animal movement studies
Bibliographic record
Abstract
Stable hydrogen isotope ratios of precipitation (deltaD(p)) show distinct geographic patterns across North America. Over the last decade, ecologists have utilized growing-season deltaD(p) patterns to study the movements of migratory animals. The accuracy and precision of such studies is, in part, contingent upon the accuracy and precision of growing-season deltaD(p) maps. Previous mapping efforts have employed simple kriging procedures to produce smooth contor maps of growing-season deltaD(p). We attempted to improve these maps by incorporating the effects of altitude on both deltaD(p) values and growing season length. This involved producing elevation-corrected monthly deltaD(p), temperature, and precipitation amount values for 1-km grid cells across the continental United States and Canada using recently developed interpolation procedures. We used a geographic information system (GIS) to calculate a weighted-average growing-season deltaD(p) value for each grid cell using deltaD(p) and precipitation amount values for all months in which the mean temperature was greater than 0 degrees C. We used seven independent data sets to compare the precision of the resulting altitude-corrected map with another that did not account for altitude. Overall, predicted deltaD(p) values from the altitude-corrected map more closely matched observed values, and correspondence was more pronounced at finer spatial scales. Digital versions of the GIS-based maps generated during this effort are available via the Internet at http://biology.unm.edu/wolf/precipitationD.htm. These deltaD(p) layers can be combined with other types of spatial information, such as species' geographic ranges and habitat associations, to further improve our understanding of animal movements.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".