Species and size-selective predation by raccoons (<i>Procyon</i> <i>lotor</i>) preying on introduced intertidal clams
Bibliographic record
Abstract
Raccoons (Procyon lotor (L., 1758)) are known for their dietary plasticity and ability to exploit new resources. We studied raccoons preying on introduced intertidal clams and hypothesized that raccoons maximized energetic profit by foraging selectively. Raccoons discarded Manila clams (Venerupis philippinarum (A. Adams and Reeve, 1850)) but selected large varnish clams (Nuttallia obscurata (Reeve, 1857)), although varnish and Manila clam densities did not differ significantly and small varnish clams were more abundant than large ones. We determined the energy content of different-sized varnish and Manila clams by subtracting the cost of cracking a clam from its soft-tissue energy. Varnish clams with less shell mass than Manila clams required less energy to open, but for their size Manila clams were more profitable. We suggest that raccoons, limited to preying on clams when the tide is low and at risk feeding on an open beach, select varnish clams because they need less handling, but maximize profit by selecting large clams. Our calculations indicate that a raccoon eating large varnish clams could obtain up to 8.4% of its daily basal metabolic needs in 10 min, making varnish clams a potentially valuable new prey resource.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".