Incorporating molecular-based functional and co-occurrence network properties into benthic marine impact assessments
Bibliographic record
Abstract
Taxonomic and functional community structures may respond differently to anthropogenic stressors. Used in combination they can provide an estimate of functional redundancy, a key component of ecosystem resilience. In this study, the utility of incorporating functional community structure and co-occurrence network properties into impact assessments of offshore oil and gas (O&G) operations on benthic bacterial communities was investigated. Sediment samples and physico-chemical data were collected along a transect at increasing distances from one exploratory drilling (ED), and one gas production and drilling (GPD) field. Bacterial community composition was determined by 16S rRNA metabarcoding. A hidden-state prediction method (PAPRICA) was used to characterize bacterial metabolic community functions. At both sites, diversity differed significantly between near-field (impacted) and far-field (non-impacted) stations, with both taxonomic and functional alpha-diversity positively affected in near-field stations at the GPD site. The opposite pattern was observed in the near-field samples of ED with lower and higher values respectively. Overall, impacted stations displayed a distinct network signature, with a lower ratio of positive interactions and signs of higher community cohesion. Community profiles from metabolic inference and co-occurrence network topology provided complementary information to taxonomic indices, which may assist with assessing the effects of O&G activities on benthic communities.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".