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Record W7095979032

SOUTHERN JOURNAL OF AGRICULTURAL ECONOMICS December, 1971 INTERTEMPORAL ALLOCATION OF GROUND WATER IN THE CENTRAL OGALLALA FORMATION: An Application of a Multistage Sequential Decision Model

2015· article· en· W7095979032 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGroundwater rechargeAquiferStock (firearms)GroundwaterIrrigationResource (disambiguation)AgricultureIrrigated agricultureSocial cost
DOInot available

Abstract

fetched live from OpenAlex

A closed underground water supply whose annual THE STUDY AREA recharge is insignificant relative to its annual with-drawal is a stock resource subject to eventual economic exhaustion. Furthermore, it is a common The Central Ogallala Formation is an unconsoli-property resource because its users tap the same reser- dated aquifer underlying approximately 17,500 voir. Economists have expressed their concern over square miles of the land area between the Arkansas the intertemporal misallocation of such fugitive re- River on the north and the South Canadian River on sources, arising from a possible divergence between the south.1 The aquifer contains about 369 million social and private costs [5, 6, 7, 8, 9]. While the acre-feet of water. It supplies practically all of the practical determination of the marginal social cost of water used for irrigation, industrial and municipal a ground water stock at different points in time is a purposes in the area. Irrigation is by far the largest formidable task, economists have suggested methods user of ground water. In 1965, an estimated 2.32 of evaluating ground water as a stock resource [5, 7, million acre-feet were pumped for irrigation in the 9]. The most complete and notable contribution is study area. The estimated volume used for industrial Burt's [4, 5] application of Bellman's multistage and municipal purposes in the same period was 0.10

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.222

Codex and Gemma teacher scores by category

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

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 head0.068
GPT teacher head0.258
Teacher spread0.190 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2015
Admission routes1
Has abstractyes

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