Assessing the Impacts of Fish In-Cage Farming and Tourism on Lake Toba’s Water Quality
Bibliographic record
Abstract
In response to the claim of bad water quality of Lake Toba for an international tourism destination, the government of North Sumatra issued two regulations that determined the improvement of water quality from mesotrophic to oligotrophic and scaling down of carrying capacity from 70 thousand to 10 thousand tons per annum.This regulation provoked conflict between the tourism industry and the fish in-cage farming industry.This study was carried out to verify as to whether the fish in-cage farming industry is the sole major factor in the deterioration of Lake Toba's water quality.Field research was carried out in 2020-2021 at 60 sampling points around Lake Toba.Identified water quality based on nutrients consisting of total nitrogen, total phosphorus, chlorophyll, and water brightness.The study concluded that fish in-cage farming was not the single major factor responsible for the lake's water deterioration.It also concluded the appropriate ceiling production capacity was 67 thousand tons per annum, and the water quality became mesotrophic.Therefore, it recommended the provincial government should change its management approach to controlling pollution entering Lake Toba from an instructive (top-down) to a multi-stakeholder approach called the co-management model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".