Quantifying <sup>87</sup>Sr/<sup>86</sup>Sr temporal stability and spatial heterogeneity for use in tracking fish movement
Bibliographic record
Abstract
The specificity and accuracy of inferred fish origin and movement relies on describing spatial heterogeneity and temporal stability of environmental signatures. But the cost and logistics of sample collection often precludes the complete quantification of environmental signature temporal stability and spatial heterogeneity. We used repeated sampling and a novel approach (Bayesian ridge regression, BRR) to quantify the temporal stability and spatial heterogeneity of 87Sr/86Sr, respectively. We explained 86% of observed variation in 87Sr/86Sr using a BRR model and estimated 87Sr/86Sr throughout the Upper North Platte River Basin with high accuracy (±0.00106). Year to year variation in 87Sr/86Sr signatures ranged from 0.00007 to 0.00073 (SD), while seasonal variation ranged from 0.00091 to 0.00134 (SD). We then assessed the specificity and discussed the accuracy of inferring movement using three scenarios of described spatial heterogeneity. Our results indicate reliable inference of fish movement requires comprehensive quantification of spatial heterogeneity and temporal variation in environmental signatures.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".