Review technical claims, research workflows and evidence quality across specialist scientific domains.
A useful evaluation does not ask a science and research experts specialist for a vague opinion. It gives them a defined user, task, evidence set and rubric so their judgement can be compared, reviewed and reused. Payment, expected time, permitted materials and the review process should also be clear before a contributor accepts the work.
The required reviewer depends on what could go wrong. Routine clarity checks may need broad domain familiarity, while safety, professional standards or high-impact decisions need demonstrable science and research experts experience and an explicit escalation path.
Reviewers identify the source, rule or observable outcome behind important scores. Written rationales make disagreement inspectable and help the project owner distinguish a genuine model failure from an underspecified instruction.
Accepted corrections should not disappear into a spreadsheet. The strongest examples become versioned cases that teams can rerun after prompt, model, tool or policy changes to see whether quality improved without introducing a new failure.
Specialists first review shared science and research experts examples and compare material differences. Calibration exposes ambiguous instructions, missing evidence and inconsistent severity rules before those problems affect a larger evaluation or training dataset.
Projects should use approved, minimised evidence and clearly exclude confidential or unrelated material. Contributors need a visible way to pause, redact or escalate when a task would require information they are not authorised to disclose or assess.
SPECIALIST SKILLS
Explore all 8 capabilities. Each has its own qualification profile, example work, evidence contract and safety boundaries.
Evaluate factual accuracy, source quality, uncertainty and whether conclusions overreach the available research.
Explore skillReview hypotheses, methods, measurements and statistical interpretation for validity and reproducibility.
Explore skillCreate and adjudicate domain-specific cases that distinguish fluent answers from genuine technical competence.
Explore skillTest claims, experimental interpretation and technical workflows across biology, biotechnology and biomedical research.
Explore skillEvaluate derivations, units, assumptions, experimental claims and technical explanations in physical-science domains.
Explore skillTest requirements interpretation, calculations, safety factors and trade-offs across mechanical, aerospace and industrial engineering tasks.
Explore skillTest whether AI interprets laboratory data consistently, detects quality issues and preserves traceability from source measurement to conclusion.
Explore skillEvaluate manuscripts, reports and explanations for factual fidelity, audience fit, reproducibility detail and responsible treatment of uncertainty.
Explore skillEXAMPLE PROJECTS
Every campaign defines its own scope, eligibility, quality checks and payment before work begins.
Every campaign defines its own scope, eligibility, quality checks and payment before work begins.
Every campaign defines its own scope, eligibility, quality checks and payment before work begins.