Reply to the comment by Franklin et al. on “Are survival rates for northern spotted owls biased?”Appears in Can. J. Zool. <b>83</b>: 1386–1390.
Bibliographic record
Abstract
We reply to Franklin et al.’s critique of our recent work in which we computed survival for northern spotted owls ( Strix occidentalis caurina (Merriam, 1898)) from sites in western Oregon and northern California based on 197 radio-collared owls. Several methods gave similar results and we noted that our estimated survival rates might be closer to the true value than those derived from mark–recapture studies. We included an errant reference to Anthony et al. (Wildl. Monogr. No. 163, pp. 1–47 (2006)) in comments about bias in prior estimates of survival and hence of λ, a mistake for which we published an erratum. In spite of our erratum, Franklin et al. correct our presumed misunderstanding of the re-parameterized Jolly–Seber methods used in the article by Anthony et al. We never intended our comments to refer to the article by Anthony et al. The commentary also states that we overestimated survival because birds that left the study area might actually have died simultaneously with radio-collar destruction. However, in our earlier paper, we stated quite clearly that the fate of virtually every bird was accounted for by tracking them down if they left the study area or until the body was found if dead. They secondarily state that birds that emigrated might have a higher mortality rate and cited as evidence a study based on four owls. We do not consider that study sufficient to determine whether mortality rates for emigrating owls may be elevated. We also dispute several other criticisms but concur with them that several issues related to owl demography could benefit from further study.
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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 | 0.013 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.037 | 0.047 |
| Insufficient payload (model declined to judge) | 0.008 | 0.012 |
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".