Temporal and spatial variation in strontium in a tropical river: implications for otolith chemistry analyses of fish migration
Bibliographic record
Abstract
Analysis of otolith strontium isotope ratios (87Sr/86Sr) is an increasingly utilized approach for studying fish migration. We analysed surface and ground water from the Daly River catchment in the wet–dry tropics of northern Australia over 2 years. Analyses of otolith 87Sr/86Sr ratios were also conducted for freshwater sooty grunter (Hephaestus fuliginosus) and the putatively diadromous diamond mullet (Liza ordensis). Spatial variation in freshwater 87Sr/86Sr was high (range: 0.71612–0.78059), and there was strong seasonality in water 87Sr/86Sr, with highest values in the wet season. Temporal variation in water 87Sr/86Sr ratios is attributed to seasonal patterns in surface runoff from geological formations with radiogenic compositions versus input from groundwater aquifers interacting with less radiogenic formations. Temporal variation in water 87Sr/86Sr ratios precluded robust inference on movement within fresh water for both species, although movement across salinity gradients by diamond mullet was clearly identified. We conclude that temporally and spatially replicated water Sr data should be a general requisite for studies that analyse otolith Sr (87Sr/86Sr, Sr/Ca, Sr/Ba) to make inferences about fish movement and migration.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".