The Animal Kingdom, Agriculture⋯ and Seaweeds
Bibliographic record
Abstract
Marine macroalgae (seaweeds), are amongst the first multicellular organisms and, as such, the precursors to land plants. By the time ‘land’ animals arrived on the scene, terrestrial plants were plentiful and varied, and herbivorous diets developed in concert with the food sources most commonly available. However, skip forward several hundred millennia, and with the advent of agriculture, approximately 10,000 years ago, dietary diversity began to change. Today, the world is experiencing increasingly higher rates of debilitating, non-communicable diseases—might there be a connection? This paper reviews scientific evidence for the judicious use of various seaweeds in the reduction of heat stress, enhanced immunity, improved growth performance, and methane reduction in animals. The extensive, (super) prebiotic effects of selected macroalgae will also be highlighted. Key studies conducted across the animal kingdom provide considerable support that there is an overwhelming need for the guided and wise applications of increased usage of selected seaweeds in feed, food and supplements. Particular attention will be paid to the bioactive components, and nutraceutical qualities, of various seaweeds, i.e., the brown, Saccharina (Laminaria) spp. and Ascophyllum nodosum, and the red alga Chondrus crispus. Suggestions are put forward for benefits to be derived from their further applications.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".