DIFFERENTIAL EFFECTS OF SEDIMENTS ON SURVIVAL AND GROWTH OF <i>FUCUS SERRATUS</i> EMBRYOS (FUCALES, PHAEOPHYCEAE)<sup>1</sup>
Bibliographic record
Abstract
Recruitment of seaweeds through small reproductive stages is limited on sediment inundated rocky shores and largely unsuccessful in soft sediment environments. Burial in sediment has several potentially negative effects for seaweed propagules, and these effects were differentiated in a laboratory experiment. We investigated how light deprivation, sediment type (grain size, organic content, and origin), and sediment chemistry (oxygen presence and toxicity through hydrogen sulfide) affected survivorship and growth of Fucus serratus L. embryos. Presence of hydrogen sulfide had overriding negative impacts on both survivorship and growth of Fucus embryos, independently of sediment type and light availability. In contrast, simple anaerobiosis generally did not impair survival or growth of the embryos. Fine sediments, 3 mm thick, significantly reduced embryo survivorship, presumably through accumulation of metabolic waste products in the immediate vicinity of the embryos as a consequence of constrained diffusion. This effect was equally pronounced in the presence of a 1‐mm layer of organically rich biodeposits. Irradiance levels did not affect survival of embryos but influenced growth. Decreasing thickness and increasing coarseness of sediments together represented a gradient of enhanced light penetration and diffusion. Growth of embryos increased along this gradient. In nature, soft sediment environments with organically enriched muds (e.g. tidal flats and salt marshes) represent habitats least favorable for colonization through small reproductive stages of seaweeds.
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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 | 0.000 | 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".