Ecological periodic tables for nekton usage of four US Pacific Northwest estuarine habitats
Bibliographic record
Abstract
We compared the species composition and abundance of the total nekton community, using the Bray–Curtis similarity coefficient, and mean total nekton, fish and crab species richness, abundance and biomass, and shrimp abundance across four intertidal habitats in a US Pacific Northwest estuary: (i) eelgrass ( Zostera marina ); (ii) burrowing mud shrimp ( Upogebia pugettensis ); (iii) burrowing ghost shrimp ( Neotrypaea californiensis ); and (iv) unvegetated sand. Field sampling was conducted during daytime high tides, and was quantitative, stratified-by-habitat, randomized, and estuary-wide. More than 10 000 nekton specimens belonging to 64 taxa were collected in 454 samples during 10 sampling periods, each approximately one-month-long (from June to November), over 3 years (1998–2000). Non-metric multidimensional scaling analyses revealed annually recurring across-habitat patterns in total nekton Bray–Curtis similarity. Two-way (habitat, year) analyses of variance revealed annually recurring across-habitat patterns on 10 indicators of nekton-habitat quality and preference. Total nekton species richness, abundance, and biomass were, respectively, on average, 8 ×, 25 ×, and 25× greater in eelgrass, 4 ×, 6 ×, and 5× greater in mud shrimp, and 2 ×, 3 ×, and 2× greater in ghost shrimp, than in sand habitat. Our findings validate the ecological relevance of our habitats to nekton, and suggest they can serve as elements in ecological periodic tables of nekton habitat usage.
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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 | 0.005 |
| 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) | 0.019 | 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".