Bibliographic record
Abstract
It's been proven by science — dogs are good for your health, FamilyMinded reported Feb. 1. In separate studies recently conducted by the Journal of Psychiatric Research and the Journal of Applied Developmental Science , researchers found that owning a dog not only made people suffering from mental health issues feel better, but it also made them more likely to help others. Additional research has shown that dog ownership also lowers blood pressure, elevates serotonin and dopamine in the brain and even lowers triglycerides and cholesterol. If you own a dog, some of this may be a given. You know how having a dog has impacted your life. But if you're still on the fence about dog ownership and are also experiencing mental health issues, dogs offer companionship and comfort, and can help ease loneliness, depression and anxiety. Some examples of the best breeds, among more than a dozen to choose from if you need a calming best friend, include border collies, Yorkshire terriers, Labrador retrievers, pugs, and standard poodles.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".