Lakes of Kenya: Scientific Expedition to the Less Explored Lakes of Kenya (SELELOK): A Research Project Proposal ( 1992-1995).
Bibliographic record
Abstract
This document proposes Scientific Expedition to the Less Explored Lakes of Kenya (SELELOK). The term ”Less explored lakes” is employed here to mean lentic inland water bodies and includes permanent to ephemeral shallow lakes, swamps and marshes; wetlands man-made dams and reservoirs in various parts of the country. The scientific Expeditions to the less explored lakes of Kenya (SELELOK) was conceived by the Board of Management of the Kenya Marine and Fisheries Research Institute in early August 1989. The Board regarded many shallow lakes in Kenya as sensitive ecosystems whose Limnology and Fisheries resources, inter-alia, have not been adequately or not at all explored. Consequently the Board stressed that particular attention should be paid to the ecosystems in which ecological problems due to climatic influences and human perturbations can already be envisaged. The main aim of the SELELOK is to carry out short but intensive surveys on the fisheries and limnology of the less explored lakes as a basis for rehabilitation, conservation and rational utilization of their aquatic resources. To achieve this aim an increase in local and international cooperation and shared use of local institution facilities and equipments is needed. It is essential that the involved institutions reach agreements concerning sharing of samples, data, publication and that an expedition team be established to deal with operational problems. The expedition will consider lakes on the mountain areas: Mt. Kenya, Elgon and Aberdares.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".