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Record W2966208233 · doi:10.15173/sciential.v1i2.2136

Immortal Hydra as a Model Organism for Metal Toxicity Studies

2019· article· en· W2966208233 on OpenAlexafffundvenue
Alice Nykolay, Aiman Shahid

Bibliographic record

VenueSciential - McMaster Undergraduate Science Journal · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Invertebrate Physiology and Ecology
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsLernaean HydraOrganismToxicityMetal toxicitySimplicityBiologyModel organismToxicologyMedicine

Abstract

fetched live from OpenAlex

Toxicology is an interdisciplinary scientific field that explores the impact, epidemiology, and treatment regimens for exposure to various toxic compounds and elements. Many toxicants such as metals have not yet been comprehensively examined, and a plethora of metal-related conditions are currently untreatable. Hydra is an immortal freshwater organism that serves as an excellent model for toxicity studies due to its natural availability, anatomical simplicity, yet comparatively complex physiology. This review will examine the significance of hydra toxicity studies, outline current experimental designs, as well as summarize the most commonly tested metals. Altogether, comprehensive toxicity studies on Hydra might provide promising breakthroughs in the understanding of toxicity-related physiology, and can be applied to clinical research and practice to ultimately improve health and wellbeing of those affected by metal-related disorders.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
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 head0.025
GPT teacher head0.274
Teacher spread0.249 · 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 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

Citations1
Published2019
Admission routes3
Has abstractyes

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