Nature-based coastal restoration: Development of an early-rearing production protocol of sugar kelp (Saccharina latissima Linnaeus) for bottom planting activities in the Gulf of St-Lawrence (Québec, Canada)
Bibliographic record
Abstract
Successful bottom planting of indigenous macroalgae Saccharina latissima aimed at coastal restoration purposes require the mass production in controlled conditions of strongly fixed, healthy sporophytes followed by optimal transfer techniques in order to ensure viability and vigor of the young seedlings about to be directly introduced in the coastal environment. Early development of S. latissima submitted to different combinations of substrate type (natural vs artificial brick-shaped substrate), gametophyte spraying method (water-based vs binder-based) and water velocity (0.1 vs 0.2 m s-1) was evaluated during a growth trial that lasted 42 days. Overall, all experimental groups (8 in triplicate) reached the targeted length of 15 mm between 35-42 days post-seeding. No strong indications that the proposed 2×2×2 factorial design generated long lasting effects on growth and development indicators were observed (thallus length, SGR and % coverage). The observation of no persistent difference in the growth response of S. latissima under all experimental conditions, demonstrates that it is well suited for mass production of seedlings. Our results and evidenced-based practices led us to conclude that the use of an artificial substrate in combination with a binder-based gametophyte pulverization and the application of a velocity 0.2 m s-1 during early-growth could be adopted in a standardized protocol. We argue that 1) artificial substrates (uniform shape, stackable and rough surface) will most likely allow better use of a vessel’s open deck space and adherence of the developing holdfast; 2) the use of a binder may slow down the dehydration of the propagules and promote adhesiveness to the substrate during rearing, handling and transfer operations and under varying flow rates or wave actions respectively and 3) highest velocity should promote the selection of propagules with strongest attachment and thus possibly limit post-transfer dislodgement. We suggest further studies should 1) focus on identifying optimal gametophyte concentration at the spraying step, in order to reduce production costs and maximise productivity of seedling operations and 2) include biomass determination (g of tissue per cm2) in combination to the semi-quantitative density evaluation (% coverage) based on image-analysis, in order to improve our global assessment of growth.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".