Trojan Horses in the Marine Realm: Characterizing Protistan Parasite Ecology in Coastal Waters
Bibliographic record
Abstract
Protists are taxonomically and metabolically diverse drivers of energy and nutrient flow in the marine environment, with recent research suggesting significant roles in global carbon cycling throughout the water column. Top-down controls on planktonic protists include grazing and parasitism, processes that both contribute to nutrient transfer and biogeochemical cycling in the global ocean. Recent global surveys of eukaryotic small subunit ribosomal RNA molecular signatures have highlighted the fact that parasites belonging to the marine alveolate order Syndiniales are both abundant and ubiquitous in coastal and open ocean environments, suggesting a major role for this taxon in marine food webs. Two coastal sites, Saanich Inlet (Vancouver Island, BC) and Salt Pond (Falmouth, MA, USA) were selected as model ecosystems to examine the impacts of Syndinian parasitism on protist communities. Data presented in this thesis combines high-resolution sampling, water chemistry (including nutrients) analyses, molecular marker gene analyses, fluorescence in situ hybridization, and modeling to address key knowledge gaps regarding syndinian ecology. Information is presented on previously undescribed putative host taxa, the prevalence of syndinian parasites and infections on different hosts in coastal waters, and a framework for modeling host-parasite interactions based on field observations.
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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 | 0.001 | 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".