Nonlinear Change in Sea Level Observed at North American Tide Stations
Bibliographic record
Abstract
Boon, J.D. and Mitchell, M., 2015. Nonlinear change in sea level observed at North American tide stations.The rate at which coastal sea level is expected to rise or fall is of considerable interest to coastal residents and managers who view changes on the time scale of a 30-year mortgage. Analysis of historical records at North American tide stations provides evidence of recent nonlinear sea-level change at this scale using relative mean sea-level (RMSL) observations. RMSL tracks local inundation risk directly without the need to correct an accepted worldwide geocentric measure—e.g., global mean sea-level rise—with locally estimated vertical rate adjustments. Published RMSL linear trends provide essential information but are routinely compared between tide stations with widely varying record lengths, thereby obfuscating nonlinear change (acceleration or deceleration) over a specific period of time. Here monthly averaged RMSL data from 45 U.S. tide stations and one Canadian tide station are analyzed from 1969 through 2014, extending a definitive period of acceleration previously noted along the U.S. NE Coast. Using a Bayesian approach to determine the joint probability of paired regression parameters for RMSL quadratic trends, probabilities for forward projections to the year 2050 based on these trends suggest continued sea-level rise will be aided by acceleration presently on the order of 0.1 to 0.2 mm/y2 in the U.S. NE and Gulf Coast regions. Deceleration ranging from −0.1 to −0.4 mm/y2 is likely to reinforce falling sea levels at specific locations on the U.S. West Coast in the near term.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".