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Record W4313815486 · doi:10.18280/ijdne.170612

Ground Water Recharge Mapping in Iraqi Western Desert

2022· article· en· W4313815486 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater and Watershed Analysis
Canadian institutionsnot available
FundersUniversity of Anbar
KeywordsGroundwater rechargeLoamGroundwaterDepression-focused rechargeHydrology (agriculture)Environmental scienceRainwater harvestingSoil waterAquiferSoil scienceGeologyGeotechnical engineeringEcology

Abstract

fetched live from OpenAlex

Local climate change and water shortage led it essential to assess the amounts and locations of groundwater recharge. To keep the Iraqi Western Desert's groundwater system sustainable. A model was developed to estimate soil moisture using artificial neural networks (ANN), geographic information systems (GIS), and remote sensing (RS). The soil needed approximately 26.54% of the total amount of rainfall to saturate voids before groundwater was recharged during the study years. The amount of recharge of groundwater was estimated depending on the water balancing method. The results showed that approximately 455,306,884 m3 of rainwater during the study years was infiltrated for groundwater recharge, nearly half of the total amount of rainfall. Sandy loam soils were most leached to recharge groundwater, while loam soils were of medium rates for groundwater recharge, and silty loam soils were the lowest rates in groundwater recharge rates.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.491

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 integrity0.0000.001
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 head0.012
GPT teacher head0.232
Teacher spread0.219 · 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