Hydrologic classification of Tanzanian rivers to support national water resource policy
Bibliographic record
Abstract
Abstract Classifying rivers into homogeneous categories based on hydrological and/or environmental attributes supports the implementation of environmental flows to sustain aquatic ecosystems and support the resource needs of society. Hydrological classifications provide decision‐makers with a pragmatic number of water management units by grouping individual rivers or river segments expected to exhibit similar biophysical responses to flow alteration. Such classifications are particularly useful across broad geographies and in data‐limited contexts, such as in Tanzania, where the legal requirement to implement environmental flows for all major waterbodies remains constrained by scant data. We present a two‐level hydrological classification of all Tanzanian basins and the Rufiji River Basin. For the Rufiji River Basin, the largest river basin in the country, we performed an inductive classification based on the availability of long‐term time series of daily average discharge. We clustered 28 gauging stations into seven classes according to ecologically relevant hydrological metrics and used boosted classification trees to predict the hydrological class of all 95,909 river segments in the basin based on environmental attributes that influence flow regimes. In the absence of consistent, readily‐available gauged flow data, we conducted a deductive classification of all Tanzanian rivers whereby segments were directly grouped by multivariate similarity using the same environmental attributes. This analysis revealed 10 river classes reflecting the diversity of ecohydrological conditions characterizing the 486,681 river segments draining in and out of Tanzania. The new hydrological classifications presented here provide the foundation to guide implementation of management practices within the water policy framework of Tanzania.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".