Lake Danao, San Francisco, Cebu as cleanest and greenest lake: Its development, problems and prospects
Bibliographic record
Abstract
Lake Danao of San Francisco, Cebu was judged as one of the cleanest and greenest lakes of the Philippines under the Gawad ng Pangulo sa Kapaligiran Lake Category. The said recognition was awarded to Lake Danao because Lake Danao, as nature s gifts to the inhabitants of San Francisco, was cared for by the people as a sign of their love of nature. Aside from this, the development, problems encountered, and the plans of the Local Government for Lake Danao, were taken into consideration. \nInterview guide administered to the different agencies of the government in Pacijan Island, LGU officials and the fisherfolk, and actual field visits to the lake were used to gather data. \nResults show that the Lake Danao met the criteria for the national lake contest for its clean water and its environment and with abundant growth of plants around it. It is free from wastes and pollutants. Washing and bathing are now prohibited in the lake and no motorboats are allowed, only the paddle boats. Two parks were established in the lake namely, the Green Lake Park and the Lake Danao Park, and an eco trail was constructed around the lake for additional attraction. \nResults further show that aside from the local tourists visiting Lake Danao, foreigners from Canada, Germany, Hongkong, Indonesia, Italy, South Korea, Malaysia, Saudi Arabia, Switzerland, Taiwan, United Kingdom, USA, and our own 'balikbayan' also visited the lake. \nProblems encountered were lack of personnel s training to manage the lake, people s negative reactions to the different lake regulations and ordinances, and lack of funds. The LGU, BFAR, DA, DENR, Cebu Technological University, and other agencies collaborated to make and implement plans for the preservation of the Lake Danao.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".