Bibliographic record
Abstract
The article investigates the communication system of living beings, especially the non-humans. The author tries to analyse that if there are any relations between the evolution of the language and the communication system of the species. The author studies the Animal Communication System by Mark Hauser. According to the investigations mostly all living beings communicate to each other with any way. Humans, animals, birds, insects and even bacteria are able to communicate with each other. This reason caused Mark Hauser to investigate Animal Communication System (ACS) decades ago. Basing on his observations in different environments and with different kinds of animals for many years he defined that ACS covers three broad categories. He named those categories as signals and gave such a division: 1) The signals that relate to individual survival; 2) The signals that relate to mating and reproduction; 3) The signals that relate to other kinds of interactions among members of the same species; they are called social signals (2, s.16). The author investigates each of these categories by giving detailed examples from different sources. The author comes to the conclusion that the non-humans do not concepts as some people think. They only have abilities that were given to them by birth. And they perform their abilities in each situation.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.028 |
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".