Bibliographic record
Abstract
In the master degree thesis, we evaluated the effect of transverse river structures on longitudinal connectivity of rivers and fish in the Sora River basin. At first, the types of transverse river structures with potential impact on fish migration in rivers were defined (weir, dam, rock-ramp, sluice gate, torrential barrier, road culvert). At the investigated Sora River basin we collected data on 150 transverse river structures, including 19 dams, 126 weirs and 5 rock-ramps. Several different methods for priority ranking of transverse river structures in order to improve longitudinal connectivity of rivers were reviewed, and based on the available data and the suitability of the considered methods, we selected the Canadian method. For the Sora River basin we calculated dendritic connectivity index – DCIp index. We also calculated the theoretical DCIp indexes for determination of transverse river structures that have the greatest impact on migration of potamodromous fish. Transverse river structures, higher than 20 cm, were considered as impassable for fish. The result of the DCIp index of the examined Sora River basin showed a high pressure of transverse river structures, impassable for fish. Based on the calculated DCIp index values, we made a priority list of transverse river structures, where the establishment of longitudinal connectivity of rivers for fish needs to be done. Also the solutions for establishment of longitudinal connectivity of rivers for fish were reviewed. In the last part of master degree thesis we covered the Slovenian legislation regarding transverse river structures and longitudinal connectivity of rivers and fish, and further guidance to improve longitudinal \nconnectivity of rivers was given.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".