MétaCan
Menu
Back to cohort
Record W6913082300 · doi:10.5282/rcc/9745

Historic Lake Level Variability and Current Disasters on the Shores of Lake Tanganyika

2023· article· en· W6913082300 on OpenAlexaff

Bibliographic record

VenueUniversitätsbibliothek der LMU · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Biodiversity
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsShoreCurrent (fluid)Climate changeGlobal warmingWater levelEnvironmental changeSubmersion (mathematics)

Abstract

fetched live from OpenAlex

Since 2019, several communities on the shores of Lake Tanganyika have been experiencing an environmental disaster triggered by a dramatic rise in the lake’s water level. Popular media reports suggest that the rise is caused by global warming. However, comparable water level rises over the last 150 years show that this only tells part of the story. Global warming may well be driving Lake Tanganyika’s current rising water level, but structural factors and policy decisions taken at local and global scales are the factors that are turning this environmental change into a disaster.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.039
GPT teacher head0.218
Teacher spread0.180 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueUniversitätsbibliothek der LMUSame topicAquatic Ecosystems and BiodiversityFrench-language works237,207