Development and evaluation of consensus-based sediment effect concentrations for polychlorinated biphenyls
Bibliographic record
Abstract
Abstract Sediment-quality guidelines (SQGs) have been published for polychlorinated biphenyls (PCBs) using both empirical and theoretical approaches. Empirically based guidelines have been developed using the screening-level concentration, effects range, effects level, and apparent effects threshold approaches. Theoretically based guidelines have been developed using the equilibrium-partitioning approach. Empirically-based guidelines were classified into three general categories, in accordance with their original narrative intents, and used to develop three consensus-based sediment effect concentrations (SECs) for total PCBs (tPCBs), including a threshold effect concentration, a midrange effect concentration, and an extreme effect concentration. Consensus-based SECs were derived because they estimate the central tendency of the published SQGs and, thus, reconcile the guidance values that have been derived using various approaches. Initially, consensus-based SECs for tPCBs were developed separately for freshwater sediments and for marine and estuarine sediments. Because the respective SECs were statistically similar, the underlying SQGs were subsequently merged and used to formulate more generally applicable SECs. The three consensus-based SECs were then evaluated for reliability using matching sediment chemistry and toxicity data from field studies, dose-response data from spiked-sediment toxicity tests, and SQGs derived from the equilibrium-partitioning approach. The results of this evaluation demonstrated that the consensus-based SECs can accurately predict both the presence and absence of toxicity in field-collected sediments. Importantly, the incidence of toxicity increases incrementally with increasing concentrations of tPCBs. Moreover, the consensus-based SECs are comparable to the chronic toxicity thresholds that have been estimated from dose-response data and equilibrium-partitioning models. Therefore, consensus-based SECs provide a unifying synthesis of existing SQGs, reflect causal rather than correlative effects, and accurately predict sediment toxicity in PCB-contaminated sediments.
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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 | 0.000 | 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 | 0.000 | 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) | 0.007 | 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 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".