Mesohabitat modelling in fish ecology: A global synthesis
Bibliographic record
Abstract
Abstract Modelling the linkage between physical habitat and aquatic organisms on multiple spatial scales has become an important tool in the management of rivers. The mesoscale (10 0 –10 2 m) represents an intermediate resolution in modelling that bridges the gap between available resources and conservation efforts for riverine species. However, existing mesohabitat classification schemes for lotic systems vary significantly in the definition of habitat types as well as in their application in the field. This article aims to provide an overview of current attempts to model the mesoscale pattern of physical habitats with a focus on fish. First, we outline descriptive, qualitative as well as objective, quantitative classification methods that are available in the literature. Next, the ecological relevance of the mesohabitat concept is being discussed, using single‐species and community‐level approaches as examples. Different modelling approaches that describe and quantify riverine mesohabitats are presented, and finally, limitations and uncertainties in the modelling process are discussed, followed by an outline of future perspectives in mesohabitat modelling.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".