Estimating size-at-harvest from Indigenous archaeological clamshell assemblages in Coastal British Columbia
Bibliographic record
Abstract
Shellfish have supported Indigenous lifeways on the Pacific Coast of North America for millennia. Despite the ubiquity of clamshells in archaeological sites, shell size measurements are rarely reported due to a lack of applicable basis for generating size estimates from fragmentary remains. We present a linear regression-based method for determining shell length from hinge and umbo measurements of littleneck ( Leukoma staminea; n = 239), butter ( Saxidomus gigantea; n = 274), and horse ( Tresus nuttallii; n = 92) clams using both contemporary and archaeological shells collected from three regions in coastal British Columbia, Canada. We examine the accuracy of these size estimations, which indicate that 83%–97% of the variability in dorsal shell length is predicted by umbo thickness and hinge length. Hinge length generated higher R2 values yet exhibited greater intra- and inter-observer error. While the predicted dorsal length for each species differed by region, this size difference was smaller than intra- and inter-observer error, suggesting broad applicability for these simple measurements. We applied these formulae to a Tseshaht First Nation archaeological clamshell assemblage ( n = 488) on western Vancouver Island spanning 3000 years and observed profiles that resemble contemporary legal size limits, which suggests the sustained use and maintenance of local shellfisheries. The accuracy of these regression models for determining shell length from fragments highlights the utility of this approach as a basis for assessing past shellfish management practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".