Can Siphon Hole Morphometrics Be Used for Population Assessments of Intertidal Soft-Shell Clams, Mya arenaria?
Bibliographic record
Abstract
Abstract Measuring infaunal population dynamics relies on destructive sampling that disturbs sediments and removes animals from their habitat. Establishing effective, non-invasive sampling methods for monitoring infaunal populations can reduce the impact of scientific sampling and facilitate efficient population assessments. Using intertidal soft-shell clams (Mya arenaria L.) in eastern Canada, we explored whether population density and size structure could be estimated from visible siphon holes. Across four sites with varying sediment characteristics and infaunal species assemblages, we predicted the presence of M. arenaria with 78–100% accuracy by visually assessing siphon holes. Smaller holes (< 7.5 mm) were more likely to be misidentified. Siphon hole count was a strong predictor of actual clam count and biomass at most sites, except the site with wet muddy sediment and high densities of other infaunal species. Siphon hole length was positively related to clam shell length and wet weight at all sites; however, relationships typically had low R2 values (< 0.35). Ultimately, visual assessments of intertidal siphon holes can be effective for estimating M. arenaria densities and size structure in some habitats. Testing the application of this method to other habitats and species is warranted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".