Unifying sources and sinks in ecology and <scp>E</scp>arth sciences
Bibliographic record
Abstract
The paired source and sink concepts are used increasingly in ecology and Earth sciences, but they have evolved in divergent directions, hampering communication across disciplines. We propose a conceptual framework that unifies existing definitions, and review their most significant consequences for the various disciplines. A general definition of the source and sink concepts that transcends disciplines is based on net flows between the components of a system: a source is a subsystem that is a net exporter of some living or non-living entities of interest, and a sink is a net importer of these entities. Sources and sinks can further be classified as conditional and unconditional, depending on the intrinsic propensity of subsystems to either produce (source) or absorb (sink) a surplus of these entities under some (conditional) or all (unconditional) conditions. The distinction between conditional and unconditional sources and sinks, however, is strongly context dependent. Sources can turn into sinks, and vice versa, when the context is changed, when systems are subject to temporal fluctuations or evolution, or when they are considered at different spatial and temporal scales. The conservation of ecosystem services requires careful consideration of the source-sink dynamics of multiple ecosystem components. Our synthesis shows that source-sink dynamics has profound consequences for our ability to understand, predict, and manage species and ecosystems in heterogeneous landscapes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".