Comment on the letter of the Society of Vertebrate Paleontology (SVP) dated April 21, 2020 regarding “Fossils from conflict zones and reproducibility of fossil-based scientific data”: the importance of private collections
Bibliographic record
Abstract
When deciding to act, the neurotransmitter dopamine is implicated in a valuation of prospective effort and reward. However, its role in dynamic effort-reward integration during action, a process central to everyday behaviour, remains unclear. In a placebo-controlled, within-subject, study, we probed the impact of increasing brain dopamine levels (150 mg of levodopa) and blocking dopamine receptors (1.5 mg of haloperidol) in the context of a novel dynamic effort task in healthy human subjects. We show that modulating homoeostatic dopamine balance distinctly alters implicit and explicit effort allocation as a function of instantaneous reward. Pharmacologically boosting dopamine enhanced motor vigour, reflected in an implicit increase in effort allocation for high rewards. Conversely, pharmacological blockade of dopamine attenuated sensitivity to differences in reward context, reflected in reduced strategic effort discounting. These findings implicate dopamine in an integration of momentary physical experience and instantaneous reward, suggesting a key role of dopamine in acting to maximise reward on the fly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.063 | 0.043 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".