Abundance data for invertebrate assemblages from intertidal mussel beds along the Atlantic Canadian coast
Bibliographic record
Abstract
This data set describes the abundance of 50 invertebrate taxa found in intertidal mussel beds along the Atlantic Canadian coast. This information resulted from a regional-scale study that investigated the effects of wave exposure on the richness and composition of invertebrate assemblages from intertidal mussel beds. Abundance data are provided for taxa representing the Annelida, Arthropoda, Bryozoa, Chordata, Cnidaria, Echinodermata, Mollusca, Nematoda, Nemertea, and Platyhelminthes. The data characterize mussel beds from wave-sheltered and wave-exposed locations spanning 315 km of the coast of Nova Scotia. Univariate and multivariate analyses revealed that the compositional structure of these invertebrate assemblages differed markedly depending on wave exposure. Overall, because of its taxonomic diversity, the inclusion of data for basal, intermediate, and top trophic levels, and the coverage of two extremes of environmental stress, this data set could be useful to test broader aspects of ecological theory. Areas of ecology that could advance using this data set are those concerning environmental stress models of community organization, abundance-occupancy relationships, species co-occurrence, species abundance distributions, dominance and rarity, spatial scales of population and community variation, and distribution of functional and phylogenetic diversity. Use of this data set for academic or educational purposes is allowed as long as the data source is properly cited. When used for academic or educational purposes, this data set should be cited using the title of this Data Paper, the names of the authors, the year of publication, and the corresponding volume and article numbers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".