Evaluation of effective shore level as a method of characterizing intertidal wave exposure regimes
Bibliographic record
Abstract
Wave splash modifies the duration and timing of aerial exposure of intertidal organisms, influencing patterns of vertical zonation, thermal stress, and the consequences of climate change. Harley and Helmuth (Limnol. Oceanogr. 48:1498‐1508, 2003) described a method for measuring effective shore level (ESL), a metric that combines the influence of wave splash and tidal regime on patterns of emersion and immersion. They identified immersion events as sharp drops in temperature recorded by submersible dataloggers and compared the tide height at the time of the temperature drop to the wave height recorded by an offshore buoy. Here we explore the generality of this method at 10 sites along the Pacific coast of North America spanning 14° of latitude. We deployed miniature temperature loggers at fixed intertidal heights at each site and recorded temperatures at intervals of 5 to 15 min for periods of up to 5 years. We use these data to explore the effects of different approaches to calculating temperature drops and wave heights, as well as variation in the buoy location, on ESL calculations. We present a software program (SiteParser) that can be used to identify temperature drops in a datalogger time series and also calculate daily and monthly summary statistics of temperature. We show that ESL parameters provide a useful metric for comparing the effects of wave action on immersion patterns within sites. We also introduce a metric of average wave run‐up that can be used to compare the effect of wave action on immersion patterns among more distant locations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".