Bibliographic record
Abstract
Abstract Quantitative ecospace models are a numerical approach to comparing the functional structure of different ecosystems on macroevolutionary time‐scales, by quantifying the distribution of functional ecological traits. Ecospace modelling has historically been restricted to a combination of visual interpretation and quantification via metrics such as mean sum of ranges. We argue that comparing ecosystem function in this way overlooks critical information about degrees of overlap and redundancy, and potentially misrepresents the role of “empty ecospace” in driving macroevolution. Fuzzy ecospace modelling (FEM) places conventional ecospace modelling within a fuzzy set‐theoretic framework, wherein functional groups are learned from the dataset, creating models which are sensitive to overlap and the role of empty ecospace. Fuzzy ecospace modelling is a machine learning program which quantifies functional ecological similarity, and uses this information to classify new taxa. It creates functional groups using a Gower dissimilarity coefficient‐based approach to the k‐medoids algorithm, and uses fuzzy discriminant analysis to classify the taxa present in another ecosystem into these clusters, based on minimal Gower dissimilarity with a fuzzy threshold. This has the effect of quantifying the similarity between these ecosystems in terms of their functional groups, accounting for total redundancy, partial redundancy/novelty and total novelty. By using fuzzy membership functions, FEM can classify taxa which are highly ecologically dissimilar (outliers with respect to all functional groups), taxa which are fully redundant (100% similarity to those in a given functional group) and taxa in‐between, which represent degrees of niche overlap. This can be used to compare the functional groups present in different ecosystems (as well as their degrees of overlap), and as a metric approach to comparing total ecological disparity. These results can be used to test models of the role of empty ecospace in macroevolutionary trends, or to investigate how ecosystems respond to global perturbations. Furthermore, it allows us to define numerically the concept of empty ecospace for n‐dimensional datasets. A cluster‐based approach to the quantification of ecospace allows for a numerical estimate of niche overlap, a value particularly difficult to quantify in fossil contexts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".