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Record W2004916466 · doi:10.2135/cropsci2005.0209

Root‐Zone Salinity

2005· article· en· W2004916466 on OpenAlexaffabout
H. Steppuhn, Martinus Th. van Genuchten, C. M. Grieve

Bibliographic record

VenueCrop Science · 2005
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsSalinityYield (engineering)MathematicsCrop yieldCropSoil salinityStatisticsAgronomyBiologyEcologyPhysics

Abstract

fetched live from OpenAlex

Six empirical functions were compared for describing the product yields of agricultural crops grown while subject to increasing levels of root‐zone salinity. The four nonlinear functions fit the test data from a spring wheat ( Triticum aestivum L., cv. Biggar) experiment conducted in Canada's Salt Tolerance Testing Facility closer than the two linear functions. Although each of the four nonlinear declining functions could reasonably describe the data, the modified compound‐discount equation recorded the lowest root mean square error and the highest R 2 value. Additional response data using the nonlinear discount function obtained from 33 separate trials averaged 11% closer in statistical fit and 45% lower in statistical error than the best linear function. The discount function { Y r = 1/(1 + [( C / C 50 ) exp( sC 50) ]} follows a sigmoidal form and relates relative yield ( Y r ) to a measure of root‐zone salinity ( C ) such as the solute concentration with an electrical conductivity of an equivalent saturated soil paste extract (EC e ). This function features two parameters, the salinity level producing 50% of the nonsaline crop yield ( C 50 ) and the absolute value of the general decline in relative yield with salinity at and near C 50 , the steepness constant ( s ). These parameters combine to form a single‐value, salinity‐tolerance index (ST‐Index) consisting of the 50% reduction in crop yield (C 50 ) plus the tendency to maintain some product yield as the crop is subjected to increasing salinity levels approaching C 50 , i.e., ST‐Index = C 50 + s ( C 50 ). The ST‐Index for the Biggar wheat registered 6.44. Approximations for C 50 and s can be derived from the threshold salinity ( C t ) and declining slope ( b ) parameters of the threshold‐slope linear response function [ Y r = 1 − b ( C − C t )]. Procedures for converting C t to C 50 and b to s offer linkages between these linear and nonlinear response function parameters, and are further explored in this paper's companion. The resulting ST‐Index‐values equal 6.56, 9.43, and 5.67 for sample field (corn, Zea mays L.), forage (alfalfa, Medicago sativa L. and falcata L.), and vegetable (radish, Raphanus sativus L.) crops, respectively.

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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.230
Teacher spread0.218 · 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

Citations132
Published2005
Admission routes2
Has abstractyes

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