Half a century (1954–2009) of dissection data of sea urchins from the North American Pacific coast (Mexico–Canada)
Bibliographic record
Abstract
Total body size, mass or linear measurements, and gonad mass or volumes have been recorded for the North American Pacific coast sea urchins Strongylocentrotus purpuratus, Mesocentrotus (Strongylocentrotus) franciscanus, and Lytechinus pictus by various workers at diverse sites and for varying lengths of time from 1954 to 2009. Some dissections included other body components such as the gut, body wall, and Aristotle's lantern, and some dissections included both wet and dry mass. There are numerous peer‐reviewed publications that have used some of these data, but some data have appeared only in graduate theses or in the gray literature. There also are data that have never appeared outside the original data sheets. Historically, data were used to describe reproductive cycles and then to compare responses to stressors such as food limitation or pollution. Differences in temperature among sites also have been explored. More recently, dissection data have linked gonad development to ocean conditions, so called bottom‐up forcing. The data set presented here is a historical record of gonad development for a common group of marine invertebrates in intertidal and nearshore environments, which can be used to test hypotheses concerning future changes associated with climate change and ocean acidification along the Pacific Coast of North America.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".