Space‐time tradeoffs in the development of precipitation‐based isoscape models for determining migratory origin
Bibliographic record
Abstract
Precipitation stable isotope patterns over continental scales provide a fundamental tool for tracking origins of migratory species. Hydrogen isotopes from rain and environmental waters are assimilated into animal tissues and may thereby reveal the location where tissues were synthesized. Predictive isotopic maps (or isoscapes) of stable hydrogen isotope values in precipitation ( δ 2 H p ) are typically generated by time‐averaging observations from a global network of stations that have been sampled irregularly in space and time. We previously demonstrated that restricting the temporal range in δ 2 H p isoscapes to biologically relevant time frames did not improve predictions of geographic origin for two migratory species in North America and Europe; rather, it decreased the accuracy of assignment. Here, we examined whether the reduction in assignment accuracy stemmed from a decrease in the number of sampling stations available to support isoscape development for shorter time periods. Multiple regression models were used to predict the hydrogen isotope composition in precipitation using isotopic measurements from each station along with a suite of independent variables. The reduction in the number of stations with δ 2 H p measurements used to estimate isoscape model parameters did not alter the accuracy and precision of assignments consistently. We also examined accuracy across a range of reduced station numbers and found that mean accuracy was affected only at very low numbers of stations, indicating that the spatial isotopic patterns in precipitation that are useful for assignment applications can be characterized with data from relatively limited data stations. The number and spatial distribution of stations may have more influence when geostatistical models are used to generate isoscapes, as they incorporate spatial correlation in the dataset. The results can be used to guide future research in understanding how data availability and constraints in creating δ 2 H p isoscapes may affect predictions of geographic origins.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".