Calibration of the Gastrointestinal Magnification Model to Predict Maximum Biomagnification Potentials of Polychlorinated Biphenyls in a Bird and Fish
Bibliographic record
Abstract
The gastrointestinal magnification (GI-magnification) model was calibrated in ring doves and Japanese koi using matched data on dietary assimilation and fecal depuration of polychlorinated biphenyls (PCBs). Mass transport parameters describing PCB flux from gut contents to organism (D(go)′; mol d(-1) Pa(-1)) and organism to gut contents (D(og); mol d(-1) Pa(-1)) were quantified to test the hypothesis that the ratio of these two terms approached unity. For birds, D(go)′/D(og) ranged from 2.9 to 6.3 and for fish the ratios ranged from 0.7 to 3.1. In both species, the ratio commonly exceeded 1. The GI-magnification model was used to predict maximum PCB biomagnification factors (BMF(max)) for each species which ranged from 18.5 to 33.8 for ring doves and 7.9 to 14.8 for Japanese koi. Chemical losses via respiration reduced steady state biomagnification factor (BMF(ss)) estimates by a negligible amount in birds, whereas for fish, predicted BMF(ss) decreased to values from 0.5 to 7.2. This study demonstrated that chemical transfer efficiency during assimilation exceeds organism/feces transfer which contributes to elevated PCB biomagnification potentials in birds and fish. Combined with reduced losses of chemical across respiratory surfaces, higher D(go)′/D(og) ratios of birds contribute to elevated biomagnification in birds over fish.
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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 | 0.001 |
| 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".