Influences of Experts' Personal Experiences in Fuzzy Logic Modeling of Atlantic Salmon Habitat
Bibliographic record
Abstract
Abstract The knowledge of scientific experts, which is regularly used in modeling, is acquired by training, education, and practical experiences that modify the experts' perceptions. Using a case study dealing with fish habitat modeling, we investigated the possible influences and potential biases imparted by some of these personal experiences. Thirty salmon experts with different backgrounds and nationalities defined fuzzy sets and fuzzy rules in a fuzzy habitat model of three Atlantic Salmon Salmo salar life stages. Weighted usable area (WUA) curves were calculated for each expert by coupling the fuzzy model with a hydraulic model applied to the Romaine River (Quebec, Canada). Experts were then split into subgroups, and three possible experiential biases were tested: the experts' main geographic region of expertise (Europe versus North America), their primary source of knowledge (fieldwork, scientific literature, or both), and their employment sector (public or private). A confidence interval was calculated around the median WUA curve for each subgroup by bootstrap resampling. A divergence in the confidence intervals (i.e., no overlap) indicated a significant influence of the tested experience. For all three considered life stages, we observed no significant impact of employment sector or knowledge source on modeled WUA. However, the experts' geographic region of expertise had a significant influence on the output of the spawning adult habitat model. Consequently, the use of local expert knowledge in modeling is recommended. Received June 16, 2014; accepted December 1, 2014
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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 | 0.009 | 0.049 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".