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Record W2493359034 · doi:10.1021/bk-2004-0872.ch009

Cadmium Accumulation in Wheat and Potato from Phosphate and Waste-Derived Zinc Fertilizers

2003· book-chapter· en· W2493359034 on OpenAlexaboutno aff
W. L. Pan, Robert G. Stevens, K. A. Labno

Bibliographic record

VenueACS symposium series · 2003
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsDiammonium phosphateFertilizerCadmiumPhosphatePhosphate fertilizerZincAgronomyAnimal scienceChemistryPhosphoriteBiologyBiochemistry

Abstract

fetched live from OpenAlex

Phosphate and zinc fertilizer sources greatly vary in cadmium concentration, depending on the fertilizer raw material source and processing. Washington state has adopted Canadian guidelines for maximum allowable metal loading rates from fertilizers. Fertilizer screening rates are application rates established to determine potential metal loading rates in WA. Screening rates of 196 kg P2O5/ha and 8.4 kg Zn/ha were defined by 1998 Fertilizer Regulation Act. A 2-y field experiment was conducted to determine effects of some P and Zn fertilizers, applied at and above WA fertilizer screening rates, on wheat and potato Cd. An irrigated sandy soil was treated with 4 P sources, ranging from 49 to 780 mg Cd/kg P, and 1 waste derived Zn source. All sources applied at the WA screening rate maintained Cd levels at or below 0.05 mg Cd/kgfw for potato tubers and 0.1 mg Cd/kgdw for wheat grain. However, excessive triple super phosphate (TSP) applications over two years (1568 and 3156 kg P2O5/ha) exceeded 0.01 mg Cd/kg in wheat grain. In potato, 784 and 1568 kg P2O5/ha rates of TSP in both years and Western diammonium phosphate (WDAP) applied at 392 kg P2O5/ha over two years approached or exceeded 0.05 mg Cd/kgfw tuber. Overall, the current WA regulations on fertilizer Cd loading appear to be adequate at the established screening rates. Growers should be advised to adhere to agronomic rates to minimize metal loading.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.223
Teacher spread0.207 · 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.

Study designBench or experimental
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

Citations4
Published2003
Admission routes1
Has abstractyes

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