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Record W2094381168 · doi:10.1080/15226514.2011.568533

Making Phytoremediation Work Better: Maximizing a Plant’s Growth Potential in the Midst of Adversity

2011· review· en· W2094381168 on OpenAlexaff
Bernard R. Glick, Jennifer C. Stearns

Bibliographic record

VenueInternational Journal of Phytoremediation · 2011
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhytoremediationPlant growthToxicantBiologyCadmiumHeavy metalsEnvironmental scienceBotanyEnvironmental chemistryBiotechnologyChemistryToxicity

Abstract

fetched live from OpenAlex

While a number of different plants can either breakdown a variety of organic contaminants or hyperaccumulate metals from the environment, even the most efficient of those plants is typically inhibited by the presence of the toxicant(s). The plant stress that is induced by the presence of various environmental toxicants typically limits a plant's growth and ultimately its ability to phytoremediate the toxicant(s). Here, it is argued that the simple strategy of adding plant growth-promoting bacteria (preferably endophytes) that reduce plant ethylene levels by ACC deaminase activity and have the ability to synthesize the phytohoromone IAA, and are used to phytoremediate various toxicants can significantly (and often dramatically) increase both plant growth and phytoremediation activity in the presence of those toxicants.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.281
Teacher spread0.216 · 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 designOther design
Domainnot available
GenreReview

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

Citations103
Published2011
Admission routes1
Has abstractyes

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