Using Relational Biology with Loop Analysis to Study the North Atlantic Biological Carbon Pump in a ‘Hybrid’ Non-Algorithmic Manner
Bibliographic record
Abstract
Biologists, philosophers, and mathematicians building upon Robert Rosen’s non-algorithmic theories of life using Relational Biology and Category Theory have continued to develop his theory and modeling approaches. There has been general agreement that the impredicative, self-referential, and complex nature of living systems negates an algorithmic approach. Rosen’s main goal was to answer, “What is Life?”. Many believe he provided the best but minimum answer using a cellular, metabolism–repair or (M, R)-system as a category-theoretic model. It has been challenging, however, to incorporate his theory to develop a fully non-algorithmic methodology that retains the essence of his thinking while creating more operational models of living systems that can be used to explore other facets of life and answer different questions. Living systems do more than the minimum in the real world beyond the confines of definition alone. For example, ecologists ask how living systems inherently mitigate existential risk from climate change and biodiversity loss through their complex self-organization. Loop Analysis, a signed graph technique, is discussed as a hybrid algorithmic/non-algorithmic methodology in Relational Biology. This methodology can be used at the ecosystem level with standard non-algorithmic field data as per McAllister’s description of the algorithmic incompressibility of empirical data of this type. An example is described showing how the North Atlantic Carbon Pump, an important planetary life support system, is situated in the plankton community and functions as a mutualistic ecosystem chimera. It captures carbon from the atmosphere as an extended (M, R)-system and processes it until it is sequestered in the marine sediments. This is an important process to alleviate climate change in magnitude equal to or larger than the sequestration of carbon on land with forests. It is suggested that the ecosystem level should replace the cellular and organismic levels as the main system unit in biology and evolution since all life exists and evolves with full functional potential in ecosystem networks and not laboratory test tubes. The plankton ecosystem is the largest after the total biosphere and consists of evolutionary links and relationships that have existed for eons of time. If there was ever a genuine robust, highly self-organized ecosystem, it would be planktonic. Severing the links in these thermodynamically open networks by focusing on lower levels of the biological hierarchy loses the critical organization of how life exists on this planet. There is no theory to regain this crucial ‘omitted’ ecological relational causality at the cell or organismal levels. At the end of the paper, some future directions are outlined.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".