A case-study evaluating river rehabilitation alternatives and habitat heterogeneity using the hydromorphological index of diversity
Bibliographic record
Abstract
Concurrent rehabilitation alternatives were evaluated for a 1900 m reach of the River Etsch in northern Italy using a recently developed Hydro Morphological Index of Diversity (HMID) model. HMID is a new tool enabling quantitative assessments of river restoration alternatives on ecologically relevant scales. The model is able to characterize both the spatial and temporal hydrodynamic variability, in relation to morphological characteristics, and thereby to assess the overall physical habitat variability imparted by different restoration alternatives.Three habitat alternatives were evaluated (boulder clusters, groynes, alternating bars) and compared to existing conditions where a prismatic channel exists. The placement of boulder clusters represented the simplest and cheapest option to implement but produced marginal improvements in the aquatic habitat. Installation of groynes resulted in moderate increases to spatial habitat variability, resulting in the largest capital investment. The introduction of alternating bars within a modestly increased channel width produced the greatest increase in physical habitat diversity and thus morphodynamic complexity while resulting in moderate capital investment construction costs compared to a full geomorphic restoration. The alternating bar rehabilitation alternative also supported the greatest habitat diversity and lowest variation in hydraulic conditions under low-flow conditions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".