Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Plants have long been excluded from the conversation regarding intelligent functioning in living things. This mindset dates back to ancient times, when plants were assigned a low-functioning and unintelligent rung on the scala naturae. In comparison to animals, plants have evolved to respond to their environment with a modular body plan, which lacks a nervous system and ‘intelligent’ organ, such as a brain. Despite this, research has demonstrated that plants are able to sense their environment, transmit sensory information throughout the entire organism, and respond to this sensory information with appropriate physiological responses. Also, plants have been shown to demonstrate aspects of learning and memory -cognitive functions once thought to be restricted to ‘intelligent’ beings (i.e. animals). The argument against plant intelligence is largely semantic-based, and stems from the concept that the word ‘intelligence’ cannot be applied to organisms which lack organs responsible for intelligent functioning. To truly appreciate the intelligent functioning of plants, we must eliminate this semantic barrier through a re-evaluation of our conventional understanding of intelligence. Perhaps this would require us to view intelligence, not as a quality unique to animals, but as a biological property, which in varying degrees is present in all life forms.
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.
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 | 0.001 | 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 it