Quantifying the effectiveness of regional habitat quality index models for predicting densities of juvenile Atlantic salmon (<i>Salmo salar</i> L.)
Bibliographic record
Abstract
Abstract – The suitability of using regional Habitat Quality Index (HQI) models for predicting distributions of juvenile salmon ( Salmo salar L.) was examined using data acquired from selected rivers within Québec, Canada. Regional HQI models were derived by identifying preferences of salmon fry and parr for the habitat properties of flow velocity, water column depth and granulometric index (an index of substrate size) for groups of rivers. These were compared with local HQI models, derived from preferences for habitat properties within individual rivers. Relationships between HQI values and densities of juvenile salmon were established through the use of nonlinear regression. In all cases, the regional HQI models were less effective for explaining distributions of juvenile salmon than local HQI models based on individual rivers. Regional HQI models were relatively ineffective when the habitat characteristics of the river to which they were applied differed greatly from the characteristics of the other rivers within the region. It is inferred that the relationship between the effectiveness of the regional HQI model and the difference in characteristics between individual rivers and those of the region may be used in an a priori determination of whether a regional HQI may be applied effectively to any given river.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".