AN IMPROVED UNDERSTANDING OF THE ALASKA COASTAL CURRENT: THE APPLICATION OF A BIVALVE GROWTH-TEMPERATURE MODEL TO RECONSTRUCT FRESHWATER-INFLUENCED PALEOENVIRONMENTS
Bibliographic record
Abstract
Abstract Shells of intertidal bivalve mollusks contain sub-seasonally to interannually resolved records of temperature and salinity variations in coastal settings. Such data are essential to understand changing land-sea interactions through time, specifically atmospheric (precipitation rate, glacial meltwater, river discharge) and oceanographic circulation patterns; however, independent temperature and salinity proxies are currently not available. We established a model for reconstructing daily water temperatures with an average standard error of ∼1.3 °C based on variations in the width of lunar daily growth increments of Saxidomus gigantea from southwestern Alaska, United States. Temperature explains 70% of the variability in shell growth. When used in conjunction with stable oxygen isotope data, this approach can also be used to identify changes in past seawater salinity. This study provides a better understanding of the hydrological changes related to the Alaska Coastal Current (ACC). In combination with δ18Oshell values, increment-derived temperatures were used to estimate salinity changes with an average error of 1.4 ± 1.1 PSU. Our model was calibrated and tested with modern shells and then applied to archaeological specimens. As derived from the model, the time interval of 988–1447 cal yr BP was characterized by ∼1–2 °C colder and much drier (2–5 PSU) summers. During that time, the ACC was likely flowing much more slowly than at present. In contrast, between 599–1014 cal yr BP, the Aleutian low may have been stronger, which resulted in a 3 °C temperature decrease during summers and 1–2 PSU fresher conditions than today; the ACC was probably flowing more quickly at that time. The shell growth–temperature model can be used to estimate seasonal to interannual salinity and temperature changes in freshwater-influenced environments through time.
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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 | 0.000 | 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 | 0.001 | 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".