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Record W2348533194

Effect of mulch and water absorbent on morphological characters and membrane permeability of potato

2010· article· en· W2348533194 on OpenAlexaff
Qin Chen

Bibliographic record

VenueGanhan diqu nongye yanjiu · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsMulchStrawHorticultureChemistryAnimal scienceAgronomyBiology
DOInot available

Abstract

fetched live from OpenAlex

The effect of mulch and water absorbent on morphological characters,leaf membrane permeability and yield were studied using potato variety Kexin 1.Eight treatments were consisted,including control group(CK), control group-water absorbent junction(CKB),straw mulch(PF),straw mulch-water absorbent junction(PFB),film mulching on furrows(UPF),film mulching on furrows-water absorbent junction(UPB),film mulching beside furrows(SPF),film mulching beside furrows-water absorbent junction(SPFB).The results were concluded that plant height and plant fresh weight showed an increasing tendency with the growth duration.The plant height of CK and PFB were significantly higher than the other treatments(P0.01) at starch accumulation stage;Fresh weight of SPB and SPFB were significantly higher than others(P0.01),but the plant height and plant fresh weight of these two treatments were lower.The value of relative membrane permeability first reduced and then increased with the growth duration,and MDA content presented basically an increasing trend.The MDA content of UPF and UPFB were much higher.The yield of PF and PFB were significantly higher than others(P0.01),and the value of UPF and UPFB were lowest.The yield of PFB remained 169.65% more than UPF,and 214.20% more than UPFB.In all,straw mulch,film mulching beside furrows and film mulching beside furrows-water absorbent junction treatments produced more potato yield.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.454

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 integrity0.0000.000
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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2010
Admission routes1
Has abstractyes

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