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Record W4283069208 · doi:10.1007/s12011-022-03331-2

Evaluation of Rare Earth Element-Associated Hormetic Effects in Candidate Fertilizers and Livestock Feed Additives

2022· review· en· W4283069208 on OpenAlexaff
Franca Tommasi, Philippe J. Thomas, Daniel Mark Lyons, Giovanni Pagano, Rahime Oral, Antonietta Siciliano, Maria Toscanesi, Marco Guida, Marco Trifuoggi

Bibliographic record

VenueBiological Trace Element Research · 2022
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsCarleton UniversityEnvironment and Climate Change Canada
FundersHORIZON EUROPE European Research CouncilUniversità degli Studi di Napoli Federico II
KeywordsLivestockHormesisRare earthCropEnvironmental chemistryChemistryYield (engineering)Feed additiveBiotechnologyAgronomyToxicologyFood scienceBiologyMineralogyEcologyBiochemistryMaterials science

Abstract

fetched live from OpenAlex

Rare earth elements (REEs) are recognized as emerging contaminants with implications in human and environmental health. Apart from their adverse effects, REEs have been reported as having positive effects when amended to fertilizers and livestock feed additives, thus suggesting a hormetic trend, implying a concentration-related shift from stimulation to inhibition and toxicity, with analogous trends that have been assessed for a number of xenobiotics. In view of optimizing the success of REE mixtures in stimulating crop yield and/or livestock growth or egg production, one should foresee the comparative concentration-related effects of individual REEs (e.g., Ce and La) vs. their mixtures, which may display distinct trends. The results might prompt further explorations on the use of REE mixtures vs. single REEs aimed at optimizing the preparation of fertilizers and feed additives, in view of the potential recognition of their use in agronomy and zootechny.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.175
GPT teacher head0.384
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations25
Published2022
Admission routes1
Has abstractyes

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