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Record W2011658631 · doi:10.1007/978-3-7643-8336-7_1

Historical milestones and discoveries that shaped the toxicology sciences

2009· review· en· W2011658631 on OpenAlexaff
Antoinette Hayes, Steven G. Gilbert

Bibliographic record

VenueProceedings of the Fourth International Symposium on Polarization Phenomena in Nuclear Reactions · 2009
Typereview
Languageen
FieldChemistry
TopicHistory and advancements in chemistry
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsSerendipityEngineering ethicsCivilizationToxicologyBiologyEngineeringEpistemologyHistoryArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

Knowledge of the toxic and healing properties of plants, animals, and minerals has shaped civilization for millennia. The foundations of modern toxicology are built upon the significant milestones and discoveries of serendipity and crude experimentation. Throughout the ages, toxicological science has provided information that has shaped and guided society. This chapter examines the development of the discipline of toxicology and its influence on civilization by highlighting significant milestones and discoveries related to toxicology. The examples shed light on the beginnings of toxicology, as well as examine lessons learned and re-learned. This chapter also examines how toxicology and the toxicologist have interacted with other scientific and cultural disciplines, including religion, politics, and the government. Toxicology has evolved to a true scientific discipline with its own dedicated scientists, educational institutes, sub-disciplines, professional societies, and journals. It now stands as its own entity while traversing such fields as chemistry, physiology, pharmacology, and molecular biology. We invite you to join us on a path of discovery and to offer our suggestions as to what are the most significant milestones and discoveries in toxicology. Additional information is available on the history section of Toxipedia (www.toxipedia.org).

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.036
GPT teacher head0.287
Teacher spread0.251 · 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 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

Citations26
Published2009
Admission routes1
Has abstractyes

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