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African lake management initiatives: The global connection

2006· article· en· W2096357824 on OpenAlex
Robert E. Hecky, Harvey A. Bootsma, Eric Odada

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLakes & Reservoirs Science Policy and Management for Sustainable Use · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Biodiversity
Canadian institutionsUniversity of Waterloo
FundersGlobal Environment Facility
KeywordsBiogeochemical cycleEnvironmental scienceClimate changeRiparian zoneSurface runoffEnvironmental protectionEcology

Abstract

fetched live from OpenAlex

Abstract There is a global dimension to lake management in Africa and elsewhere that will require a concerted action not only from individual riparian states, but also from regional, continental and global communities. The current global lake threats arise from climate change, regional land degradation and semivolatile contaminants, and share the common feature that the atmosphere is the vector that spreads their impacts over large areas and to many lakes. The Great Lakes of Africa (Malawi, Victoria and Tanganyika) are particularly sensitive to these problems because of their enormous surface areas, slow water flushing rates, and the importance of direct rainfall in their water budgets. Their response times might be slow to yield a detectable change and, unfortunately, their recovery times might also be slow. It is possible for atmospheric effects to act antagonistically to the impacts of catchment change, but antagonistic effects could become synergistic in the future. Improved understanding of the physical dynamics of these lakes, and development of models linking their physical and biogeochemical behaviour to regional, mesoscale climate models, will be necessary to guide lake managers.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.239
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it