Size, growth, and density data for shallow‐water sea urchins from Mexico to the Aleutian Islands, Alaska, 1956–2016
Bibliographic record
Abstract
Size, growth, and density have been studied for North American Pacific coast sea urchins Strongylocentrotus purpuratus, S. droebachiensis, S. polyacanthus, Mesocentrotus (Strongylocentrotus) franciscanus, Lytechinus pictus, Centrostephanus coronatus, and Arbacia stellata by various workers at diverse sites and for varying lengths of time from 1956 to present. Numerous peer-reviewed publications have used some of these data but some data have appeared only in graduate theses or the gray literature. There also are data that have never appeared outside original data sheets. Motivation for studies has included fisheries management and environmental monitoring of sewer and power plant outfalls as well as changes associated with disease epidemics. Studies also have focused on kelp restoration, community effects of sea otters, basic sea urchin biology, and monitoring. The data sets presented here are a historical record of size, density, and growth for a common group of marine invertebrates in intertidal and nearshore environments that can be used to test hypotheses concerning future changes associated with fisheries practices, shifts of predator distributions, climate and ecosystem changes, and ocean acidification along the Pacific Coast of North America and islands of the north Pacific. No copyright restrictions apply. Please credit this paper when using the data.
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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 | 0.003 | 0.002 |
| 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) | 0.002 | 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".