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Record W2369180112

Impact on Content of Thick Albumen of Variety of Different Altfalfas of Applying Fertilizer is Old Great

2006· article· en· W2369180112 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueActa Agriculturae Boreali-Sinica · 2006
Typearticle
Languageen
FieldEngineering
TopicPolymer-Based Agricultural Enhancements
Canadian institutionsnot available
Fundersnot available
KeywordsFertilizerPotashAgronomyPhosphate fertilizerPotassiumMathematicsUreaAnimal scienceRidgeChemistryBiology
DOInot available

Abstract

fetched live from OpenAlex

Analyse different to apply fertilizer to A WL232HQ,Ao Chinese alfalfa thick albumen influence of content, money of ridge and alfalfa of American, alfalfa of Canada, and different to apply fertilizer different alfalfa thick albumen difference of content under variety under the system,from analyse different to apply fertilizer to contrast difference indicate a best one apply fertilizer scheme 60 urea different variety.33kg/hm2, over calcium phosphate are 90.78kg/hm2,sulphuric acid potassium are132.00kg/hm2,the supreme content of thick albumem: American alfalfa WL232HQ is 22.20%, the ridge money is 21 in Canadian alfalfa A.21.863%, the Chinese alfalfa of AoHan is 20.098%, Learn American alfalfa WL232HQ in apply fertilizer thick albumen content high variety than other have under the terms while being the same kind of.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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