INFLUENCES OF PHYSICAL AND BIOLOGICAL VARIABLES ON SOFTSHELL CLAM (MYA ARENARIA LINNEAUS 1758) BURIAL
Bibliographic record
Abstract
Stock enhancement of the infaunal bivalve, Mya arenaria, is becoming an increasingly important strategy for fisheries managers and the clamming industry in eastern Canada and the New England states. There is also a growing interest towards softshell clam culture. A fast burial after the seeding should decrease (1) passive dispersion by currents and waves; (2) exposure to extreme changes in temperature; and (3) predation by crabs, flatfishes, birds, and similar. Thus, a fast burial could reduce losses shortly after seeding and possibly benefit later harvests. However, little is known about factors acting on burial. This study provides clam growers in our area with general guidelines to better manage their seeding operations. Five factors possibly influencing the burial rate were examined in the present study: clam size, seeding density, emersion period prior to seeding, substratum softening prior to seeding, and seasonal period. Most experiments were performed in field conditions on sandy beaches in Iles-de-la-Madeleine, (southern Gulf of St. Lawrence). Only clam size and seasonal period showed significant effects. Clam size (15–40 mm SL) was inversely related to burial rate. Clams buried faster in late August when water temperature reached 23°C and then slowed down steadily as temperature dropped to 7°C in early October. An increase in clam density from 100–350 clams (25–30 mm SL) · m−2 had no negative effects on burial rate as well as emersion periods up to 4 h prior to seeding. Softening of the sandy substratum had no positive effects on burial rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".