Bibliographic record
Abstract
Editorial on the Special Issue Substance abuse and early developmentWhile misuse of psychoactive substances can be traced back to pre-historic times, their effects on invisible target-such as the developing fetus-only gained recognition within the last century [1][2][3][4].Initial seminal findings in humans and laboratory rodents quickly grew from isolated observations to well-planned, multidisciplinary studies.Despite the involvement of numerous methodologies, large teams of researchers, and widely different experimental organisms, there has been an unusual consensus on the meaning of findings: principally that prenatal exposure to drugs of misuse has deleterious consequences for the developing fetus.The mechanisms by which prenatal exposure to psychoactive substances alters developmental trajectories are variable, from epigenetic alterations to fine-tuning of blood supply to the brain [5,6].In some cases, such as during cannabis exposure, the impact is exacerbated by the vast presence and activity of the endocannabinoid system during early development (Fride, 2008; [7]).In others, such as with alcohol, efforts for elucidating potential mechanisms of alcohol-driven alterations and thus, identifying venues for novel therapeutic options are hindered by the chemical's simple structure and the abundant sensing sites it can bind to.Additional challenges are presented by the growing trend of substance co-use, such as in cases of simultaneous consumption of alcohol and marijuana [8] or opioid and stimulant products [9].In these instances, in addition to the effects of individual drugs, their potential interactions must be considered.The current Special Issue aims at describing recent advances in our understanding of how maternal and paternal substance abuse affects early development of progeny.Contributions into this Special Issue span from methodological advancements, into reviews of current standing in the field, original research report, and novel diagnostic tools.Specifically, Mbolle et al. take the reader on a journey into modern technology for high-resolution imaging of prenatal development.While this task is certainly streamlined in obstetrics clinics, major challenges remain at the bench, as current resolution capabilities are often below the thresholds needed for accurate visualization of fetal structures within small rodent species such as mice which are used for mechanistic studies.Development of deep-tissue high-resolution imaging and accompanying
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.039 | 0.026 |
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".