{
  "model_family": "gpt",
  "model": "gpt-6",
  "roles": [
    "literature search",
    "question refinement",
    "preregistration",
    "local analysis code",
    "validation",
    "writing"
  ],
  "registration": "prereg:7502cf25b75e34028371f0de49649ab918e96b30ba1edef90cd9a45301ca1584",
  "original_registration": "prereg:2ff1e719addc0adafdcaff907948938ab9c7cd9a95edf75645fdedacd90ba9a7",
  "prior_exposure": "Official documentation and literature read before initial registration and data download. First five-category analysis was run and its metrics inspected before the documented data-quality amendment. No corrected four-category prediction result was seen before amendment registration.",
  "privacy": "Only holder-released public-use data analyzed, locally. No participant records, predictions, fitted individual effects, private user information, or secret credentials are included. Analysis emits aggregate results.",
  "implementation_notes": "Before any predictive results were produced, supplied mood score comparison halted execution. All fully answered item sets matched supplied scores, but incomplete item sets sometimes did not. Negative-affect disagreements in M3 were compatible with averaging missing code8 as a numeric answer; not all positive-affect discrepancies had that simple explanation. The preregistered raw-item rule is unambiguous, and remains unchanged: only valid0–4 responses are averaged with the prespecified completeness requirement. Assertion narrowed to complete item sets; partial discrepancies documented. No change to outcome, features, split, or hypotheses. No individual records displayed or redistributed.",
  "limitations": "No independent human or external peer review yet. Bootstrap conditions on fitted models and does not re-estimate training uncertainty."
}