Benthic Spat Collection of Softshell Clams (<i>Mya arenaria</i>Linnaeus, 1758) using Mats
Bibliographic record
Abstract
Supply of softshell clam (Mya arenaria) seed for culture and restoration usually comes from hatcheries or through transfers from one site to another. Natural spat collection using Astro-Turf mats installed directly onto the sediments could be a viable alternative. Here, we describe a series of experiments to examine the performance of mats for benthic collection, and how to use them efficiently. Interannual variability was studied for an 8-y period (2002 to 2009) at a commercial clam culture site located in the Havre-aux-Maisons lagoon (Îles-de-la-Madeleine, southern Gulf of St. Lawrence). Spat collection (clams, 2.5–15 mm) ranged from 883–2,422 clams/m2 (overall mean, 1,599 clams/m2). Throughout the years, clams on the mats were 7 times more abundant than in the nearby sediments. Their mean shell length varied from 7.3–9.9 mm relative to the year. Mats were deployed during an extended 5-wk period (early June to early July), and also retrieved during an extended 5-wk period (early September to early October) with no negative effect on collection success. Indeed, spat abundance was higher when mats were retrieved later in the season (late September to early October). Spat collection was greater at the mid to upper intertidal level (1,551–2,622 clams > 2.5 mm/ m2) compared with the lower intertidal (near-subtidal) level (586 clams > 2.5 mm/m2). Indeed, the lowest abundances and the smallest sizes were observed on mats deployed at the lower intertidal level. Mats were installed side-by-side in groups of 35 (7 × 5), with no negative impact on collection success compared with individual mats. Last, spat collection was examined at two different sites within the lagoon for 4 y. Although the overall mean number of clams was similar at both sites (1,538 clams > 2.5 mm/m2 vs. 1,503 clams > 2.5 mm/m2), collection success varied among sites (951–1,916 clams > 2.5 mm/m2 vs. 552–2,938 clams > 2.5 mm/m2) from year to year. Therefore, collection success with mats should be investigated for any new site and for several years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 0.001 |
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".