MétaCan
Menu
Back to cohort
Record W4402087735 · doi:10.31857/s0869587324050067

Unfulfilled water impact forecasts, plans and projects

2024· article· en· W4402087735 on OpenAlexaboutno aff
Н. И. Коронкевич, Е. А. Барабанова, I. S. Zaitseva, G. M. Chernogaeva

Bibliographic record

VenueВестник Российской академии наук · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceBusinessEnvironmental planning

Abstract

fetched live from OpenAlex

The article presents a retrospective review of some forecasts, plans and projects of anthropogenic impact on water resources that did not materialize, were not implemented by the planned date or were incomplete. Values of full (water intake) and irrevocable water consumption for 2000, predicted in the 1960s and 1970s by well-known domestic and foreign researchers, are compared with the actual water consumption in 2000 in the world, the USA, and our country. It is shown that most of water consumption forecast parameters turned out to be significantly higher than the actual one, which gives reason to consider these forecasts were not borne out. In the Water Strategy of the Russian Federation, developed in 2009 for the period up to 2020, these values were also significantly overestimated (by tens of percent). Incomplete implementation of various programs did not lead to the expected significant improvement in the water quality of rivers, including the Volga, and reservoirs in Russia. Such failed projects as NAWAPA in the USA and Canada, projects of interzonal redistribution of water resources in the USSR, projects of Nizhneobskaya and Turukhanskaya (Evenkijskaya) hydroelectric power plants, partially implemented projects of Cheboksary and Nizhnekamsk hydroelectric power plants, as well as a number of others are considered. Among the main reasons for unsuccessful forecasts, unfulfilled plans and projects are the lack of reliable data, incomplete knowledge about laws of nature and society development, financial problems, environmental demands, and the dramatically changed economic and political situation.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.009
GPT teacher head0.248
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueВестник Российской академии наукSame topicFlood Risk Assessment and ManagementFrench-language works237,207