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Spatio-Temporal Distributions and Environmental Safety Threshold of Cropland Fertilization of Jiangsu Province, China

2014· article· en· W2163068520 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced materials research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Quality and Pollution
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHectareHuman fertilizationFertilizerEnvironmental scienceCroppingAgricultureChinaEutrophicationMultiple croppingPopulationMathematicsAgronomyToxicologyGeographyNutrientBiologyEcologyDemography

Abstract

fetched live from OpenAlex

Correlations and non-linear regression analyses were used to determine the optimal rate of fertilization that would maximize production of grain while minimizing effects on the environment in Jiangsu Province, China. Correlation coefficients between the amounts of cereal grain produced and rates of fertilization were 0.880, 0.606, and 0.212 for the periods 1970-1983, 1984-1997, 1998-2011, respectively. Current amounts of chemical fertilization used are causing adverse effects on the environment. By use of simulation analyses, it was determined that 550 - 600 kilogram of fertilizers (a mix of N, P 2 O 5 , K 2 O) per hectare is the upper limit amount of fertilization that balances production and potential for eutrophication in Jiangsu. Amounts of fertilizer applied are greater in the North of Jiangsu Province with three-fold more fertilizers applied than in the south. Factors such as proportion of farmers in the regional population, incomes of farmers, multiple cropping index, proportion of land irrigated, proportion of land in agricultural production all influence the amount of fertilizers applied in this region.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.288
Teacher spread0.271 · 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