<i>Fascioloides magna</i> in Moose (<i>Alces alces</i>) From Elk Island National Park, Alberta
Bibliographic record
Abstract
Thirty-seven adult female moose (Alces alces) from 2 distinct but adjacent populations in Elk Island National Park (EINP), Alberta, Canada (19 in north EINP and 18 in south EINP), were fitted with mortality-sensing VHF radio-collars, and radio signals were acquired daily to ascertain mortality status. At capture, serum, whole blood, and feces were collected; pregnancy was determined; teeth were aged by visual inspection; and a portion of liver was assessed by ultrasound examination. Postmortem examination was conducted on 20 suitable carcasses. Clinical pathological abnormalities, including eosinophilia, polycythemia, elevated levels of liver enzymes in serum, hemoglobin, hematocrit, and red blood cell distribution, and liver damage as seen in ultrasound images occurred only in moose from north EINP. Infected moose had 4.7 ± 4.8 Fascioloides magna flukes per liver (mean ± SD). The proportion of moose pregnant at capture was similar in both populations (74% in north EINP, 61% in south EINP). Proportional mortality was significantly higher in moose from the north (68%) than the south (32%). Fascioloides magna was associated as a cause of death in 7 of 14 (50%) moose in the north where cause of death was determined, while predation (n = 1), acute toxemic syndrome (n = 3), dystocia (n = 1), and roadkill and undetermined causes (n = 3) were additional causes of mortality. F. magna was associated with poor body condition and was a major cause of mortality in north EINP but not south EINP, despite very similar habitat and proximity, suggesting a significant role for these flukes in affecting health and viability of naturally infected moose populations.
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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 | 0.001 | 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) | 0.000 | 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".